Python 3
import pandas as pddf1=pd.read_csv('train.csv')df2=pd.read_csv('test.csv')print(df1.head()) PassengerId Pclass Name \
0 1 3 Braund, Mr. Owen Harris
1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th...
2 3 3 Heikkinen, Miss. Laina
3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel)
4 5 3 Allen, Mr. William Henry
Sex Age SibSp Parch Ticket Fare Cabin Embarked \
0 male 22.0 1 0 A/5 21171 7.2500 NaN S
1 female 38.0 1 0 PC 17599 71.2833 C85 C
2 female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 female 35.0 1 0 113803 53.1000 C123 S
4 male 35.0 0 0 373450 8.0500 NaN S
Survived
0 0
1 1
2 1
3 1
4 0
print(df2.head()) PassengerId Pclass Name Sex \
0 892 3 Kelly, Mr. James male
1 893 3 Wilkes, Mrs. James (Ellen Needs) female
2 894 2 Myles, Mr. Thomas Francis male
3 895 3 Wirz, Mr. Albert male
4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female
Age SibSp Parch Ticket Fare Cabin Embarked Survived
0 34.5 0 0 330911 7.8292 NaN Q 0
1 47.0 1 0 363272 7.0000 NaN S 1
2 62.0 0 0 240276 9.6875 NaN Q 0
3 27.0 0 0 315154 8.6625 NaN S 0
4 22.0 1 1 3101298 12.2875 NaN S 1
df=pd.concat([df1,df2],axis=0)df.head()| PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | Survived | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S | 0 |
| 1 | 2 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | 1 |
| 2 | 3 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S | 1 |
| 3 | 4 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S | 1 |
| 4 | 5 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S | 0 |
df=df.drop(['PassengerId'],axis=1)print(df) Pclass Name Sex Age \
0 3 Braund, Mr. Owen Harris male 22.0
1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0
2 3 Heikkinen, Miss. Laina female 26.0
3 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0
4 3 Allen, Mr. William Henry male 35.0
5 3 Moran, Mr. James male NaN
6 1 McCarthy, Mr. Timothy J male 54.0
7 3 Palsson, Master. Gosta Leonard male 2.0
8 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0
9 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0
10 3 Sandstrom, Miss. Marguerite Rut female 4.0
11 1 Bonnell, Miss. Elizabeth female 58.0
12 3 Saundercock, Mr. William Henry male 20.0
13 3 Andersson, Mr. Anders Johan male 39.0
14 3 Vestrom, Miss. Hulda Amanda Adolfina female 14.0
15 2 Hewlett, Mrs. (Mary D Kingcome) female 55.0
16 3 Rice, Master. Eugene male 2.0
17 2 Williams, Mr. Charles Eugene male NaN
18 3 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0
19 3 Masselmani, Mrs. Fatima female NaN
20 2 Fynney, Mr. Joseph J male 35.0
21 2 Beesley, Mr. Lawrence male 34.0
22 3 McGowan, Miss. Anna "Annie" female 15.0
23 1 Sloper, Mr. William Thompson male 28.0
24 3 Palsson, Miss. Torborg Danira female 8.0
25 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0
26 3 Emir, Mr. Farred Chehab male NaN
27 1 Fortune, Mr. Charles Alexander male 19.0
28 3 O'Dwyer, Miss. Ellen "Nellie" female NaN
29 3 Todoroff, Mr. Lalio male NaN
.. ... ... ... ...
388 3 Canavan, Mr. Patrick male 21.0
389 3 Palsson, Master. Paul Folke male 6.0
390 1 Payne, Mr. Vivian Ponsonby male 23.0
391 1 Lines, Mrs. Ernest H (Elizabeth Lindsey James) female 51.0
392 3 Abbott, Master. Eugene Joseph male 13.0
393 2 Gilbert, Mr. William male 47.0
394 3 Kink-Heilmann, Mr. Anton male 29.0
395 1 Smith, Mrs. Lucien Philip (Mary Eloise Hughes) female 18.0
396 3 Colbert, Mr. Patrick male 24.0
397 1 Frolicher-Stehli, Mrs. Maxmillian (Margaretha ... female 48.0
398 3 Larsson-Rondberg, Mr. Edvard A male 22.0
399 3 Conlon, Mr. Thomas Henry male 31.0
400 1 Bonnell, Miss. Caroline female 30.0
401 2 Gale, Mr. Harry male 38.0
402 1 Gibson, Miss. Dorothy Winifred female 22.0
403 1 Carrau, Mr. Jose Pedro male 17.0
404 1 Frauenthal, Mr. Isaac Gerald male 43.0
405 2 Nourney, Mr. Alfred (Baron von Drachstedt")" male 20.0
406 2 Ware, Mr. William Jeffery male 23.0
407 1 Widener, Mr. George Dunton male 50.0
408 3 Riordan, Miss. Johanna Hannah"" female NaN
409 3 Peacock, Miss. Treasteall female 3.0
410 3 Naughton, Miss. Hannah female NaN
411 1 Minahan, Mrs. William Edward (Lillian E Thorpe) female 37.0
412 3 Henriksson, Miss. Jenny Lovisa female 28.0
413 3 Spector, Mr. Woolf male NaN
414 1 Oliva y Ocana, Dona. Fermina female 39.0
415 3 Saether, Mr. Simon Sivertsen male 38.5
416 3 Ware, Mr. Frederick male NaN
417 3 Peter, Master. Michael J male NaN
SibSp Parch Ticket Fare Cabin Embarked \
0 1 0 A/5 21171 7.2500 NaN S
1 1 0 PC 17599 71.2833 C85 C
2 0 0 STON/O2. 3101282 7.9250 NaN S
3 1 0 113803 53.1000 C123 S
4 0 0 373450 8.0500 NaN S
5 0 0 330877 8.4583 NaN Q
6 0 0 17463 51.8625 E46 S
7 3 1 349909 21.0750 NaN S
8 0 2 347742 11.1333 NaN S
9 1 0 237736 30.0708 NaN C
10 1 1 PP 9549 16.7000 G6 S
11 0 0 113783 26.5500 C103 S
12 0 0 A/5. 2151 8.0500 NaN S
13 1 5 347082 31.2750 NaN S
14 0 0 350406 7.8542 NaN S
15 0 0 248706 16.0000 NaN S
16 4 1 382652 29.1250 NaN Q
17 0 0 244373 13.0000 NaN S
18 1 0 345763 18.0000 NaN S
19 0 0 2649 7.2250 NaN C
20 0 0 239865 26.0000 NaN S
21 0 0 248698 13.0000 D56 S
22 0 0 330923 8.0292 NaN Q
23 0 0 113788 35.5000 A6 S
24 3 1 349909 21.0750 NaN S
25 1 5 347077 31.3875 NaN S
26 0 0 2631 7.2250 NaN C
27 3 2 19950 263.0000 C23 C25 C27 S
28 0 0 330959 7.8792 NaN Q
29 0 0 349216 7.8958 NaN S
.. ... ... ... ... ... ...
388 0 0 364858 7.7500 NaN Q
389 3 1 349909 21.0750 NaN S
390 0 0 12749 93.5000 B24 S
391 0 1 PC 17592 39.4000 D28 S
392 0 2 C.A. 2673 20.2500 NaN S
393 0 0 C.A. 30769 10.5000 NaN S
394 3 1 315153 22.0250 NaN S
395 1 0 13695 60.0000 C31 S
396 0 0 371109 7.2500 NaN Q
397 1 1 13567 79.2000 B41 C
398 0 0 347065 7.7750 NaN S
399 0 0 21332 7.7333 NaN Q
400 0 0 36928 164.8667 C7 S
401 1 0 28664 21.0000 NaN S
402 0 1 112378 59.4000 NaN C
403 0 0 113059 47.1000 NaN S
404 1 0 17765 27.7208 D40 C
405 0 0 SC/PARIS 2166 13.8625 D38 C
406 1 0 28666 10.5000 NaN S
407 1 1 113503 211.5000 C80 C
408 0 0 334915 7.7208 NaN Q
409 1 1 SOTON/O.Q. 3101315 13.7750 NaN S
410 0 0 365237 7.7500 NaN Q
411 1 0 19928 90.0000 C78 Q
412 0 0 347086 7.7750 NaN S
413 0 0 A.5. 3236 8.0500 NaN S
414 0 0 PC 17758 108.9000 C105 C
415 0 0 SOTON/O.Q. 3101262 7.2500 NaN S
416 0 0 359309 8.0500 NaN S
417 1 1 2668 22.3583 NaN C
Survived
0 0
1 1
2 1
3 1
4 0
5 0
6 0
7 0
8 1
9 1
10 1
11 1
12 0
13 0
14 0
15 1
16 0
17 1
18 0
19 1
20 0
21 1
22 1
23 1
24 0
25 1
26 0
27 0
28 1
29 0
.. ...
388 0
389 0
390 0
391 1
392 0
393 0
394 0
395 1
396 0
397 1
398 0
399 0
400 1
401 0
402 1
403 0
404 0
405 0
406 0
407 0
408 1
409 1
410 1
411 1
412 1
413 0
414 1
415 0
416 0
417 0
[1309 rows x 11 columns]
i=0name=[]while i<len(df): #print(df.iloc[i]['Name'].type) if "Mrs." in df.iloc[i]['Name']: name.append(str(1)) print("a") elif 'Mr.' in df.iloc[i]['Name']: name.append(str(2)) print("b") elif "Miss." in df.iloc[i]['Name']: name.append(str(3)) print("c") elif "Master." in df.iloc[i]["Name"]: name.append(str(4)) print("d") else: name.append(str(5)) i+=1df['name']=name b a c a b b b d a a c c b b c a d b a a b b c b c a b b c b a c b b b b b c c a a b c c b b c b a d b a a b b c b c d b c b d b d a b c b b c b b b b b b d c b b c b c a b b c b b b b b b b b b a b c b b b b b c b b c b c b c c b b b b c b b b c b d b b c b b b a a b b c b b b a c a b b b b c b a b b b b c b b d b a b b d d a a b b b d c b b b d c b b c b d d c b a b b b a b c d a c b b c c b b b b b c b b c b b c b b b c c b c b b b b b b b b b b c a b b c b c b c b b c c b b b c a b b a b b a a a c c a b d b b c b b b a c b b a b c c c b d a b b b b b b b b b c c a b c b b b c b a c b b c b d c a b c c c a b b c a c a b b c a b c b a a c c b b b a b b c b b d c b b b c c a d b b b b b b b c c c c b b a b b b a a c b b b b c a c b b b c c b a b b d c b c b b b c a b c b a b b c b c b b d b c b b c b b a a c b c b b b a b b a c b b b a a b b c c a b b a b b b d c b c b b b b b b b a c b b b b b b b b b b c b b a a c b b b b c d b b a b c a b b d b b b b b b c b a b b c c c c b a b b b b b b a b b a b a b c b b a b b c b b b c b b a c c c b c c c c b b b a b b d b b b b c b b a a b b b b c b b a b b b a b c b b c a a b c a b b b c b b b b b a b c b b c b b a b b b b b b b a c a b c b b c b a c b b b b b b b c b b b b b c c b b a b b c b c b b b c b c b c c b b a b b b b b b b b b b a a b b b b b b c a b c b b b b b b b b c b c b b b b c b b a b c b b b a b c d b b b b b c c b b c b b b b b a c b c c b b b b b a b b b b b c b b b c b b c d b b a d b b b b b b a b a c b b b b a b a b b c b a c a b b b b c d d b b b c b b b a b a b a d d b b b c b a b b b c b b c b b d a b a d b b d b a a d b b b c b b b b b b c b b b b b a d b c c a a a b a b b b a c b a c b b d b a b b a c b b b a a b c b b a c c b b b a b b a b c b a b b b a b a a b b c a b d a b a b c b b b b b a a b b c c b b b b b a a b b b a a b b c c b d b b b c b b b c d a c b b a c b c b c b b a b c d b b b b b c c d a b a b b b a b c b a b b b a b b b b b b c c c a b b c b a c b a b b c b c b b b a b b b b b c b c c b b b b b b b a b b a d b c c b a c d c b a b b a c b b b b b c c b a a b b a b a b c b c b b b d b d b d c b a c d c b b c b c b b b b a c b c b a b c b a b a a b c b b b c b b b b b b c a a a b b d b a b a a c b b b b b b b c b b b a c b b b b c b b b a c b c b b b b c d c c c b b b b b b c b b b c b b b b b b b c a b d b a b b b c a c b b b b b b b b c b c b b b a b b a b b b b b d b b b a d c b a b c a b b b c b a b b a d a a b a a b c a b b c b b a c c b b d b b a a b c b b b d b a d b b a b a b b c b c b b b b b c c c a c b b b d
df=df.drop(['Name'],axis=1)df.head()| Pclass | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | Survived | name | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3 | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S | 0 | 2 |
| 1 | 1 | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | 1 | 1 |
| 2 | 3 | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S | 1 | 3 |
| 3 | 1 | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S | 1 | 1 |
| 4 | 3 | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S | 0 | 2 |
i=0siblings=[]df.fillna(0)while i<len(df): if df.iloc[i]['SibSp']>0: siblings.append(str(1)) else: siblings.append(str(0)) i+=1df=df.drop(["SibSp"],axis=1)df["Siblings"]=siblingsdf.head()| Pclass | Sex | Age | Parch | Ticket | Fare | Cabin | Embarked | Survived | name | Siblings | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3 | male | 22.0 | 0 | A/5 21171 | 7.2500 | NaN | S | 0 | 2 | 1 |
| 1 | 1 | female | 38.0 | 0 | PC 17599 | 71.2833 | C85 | C | 1 | 1 | 1 |
| 2 | 3 | female | 26.0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S | 1 | 3 | 0 |
| 3 | 1 | female | 35.0 | 0 | 113803 | 53.1000 | C123 | S | 1 | 1 | 1 |
| 4 | 3 | male | 35.0 | 0 | 373450 | 8.0500 | NaN | S | 0 | 2 | 0 |
i=0parent=[]df.fillna(0)while i<len(df): if df.iloc[i]['Parch']>0: parent.append(str(1)) else: parent.append(str(0)) i+=1df=df.drop(["Parch"],axis=1)df["Parent"]=parentdf.head()| Pclass | Sex | Age | Ticket | Fare | Cabin | Embarked | Survived | name | Siblings | Parent | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3 | male | 22.0 | A/5 21171 | 7.2500 | NaN | S | 0 | 2 | 1 | 0 |
| 1 | 1 | female | 38.0 | PC 17599 | 71.2833 | C85 | C | 1 | 1 | 1 | 0 |
| 2 | 3 | female | 26.0 | STON/O2. 3101282 | 7.9250 | NaN | S | 1 | 3 | 0 | 0 |
| 3 | 1 | female | 35.0 | 113803 | 53.1000 | C123 | S | 1 | 1 | 1 | 0 |
| 4 | 3 | male | 35.0 | 373450 | 8.0500 | NaN | S | 0 | 2 | 0 | 0 |
df['Age'].fillna(df['Age'].mean())0 22.000000
1 38.000000
2 26.000000
3 35.000000
4 35.000000
5 29.881138
6 54.000000
7 2.000000
8 27.000000
9 14.000000
10 4.000000
11 58.000000
12 20.000000
13 39.000000
14 14.000000
15 55.000000
16 2.000000
17 29.881138
18 31.000000
19 29.881138
20 35.000000
21 34.000000
22 15.000000
23 28.000000
24 8.000000
25 38.000000
26 29.881138
27 19.000000
28 29.881138
29 29.881138
...
388 21.000000
389 6.000000
390 23.000000
391 51.000000
392 13.000000
393 47.000000
394 29.000000
395 18.000000
396 24.000000
397 48.000000
398 22.000000
399 31.000000
400 30.000000
401 38.000000
402 22.000000
403 17.000000
404 43.000000
405 20.000000
406 23.000000
407 50.000000
408 29.881138
409 3.000000
410 29.881138
411 37.000000
412 28.000000
413 29.881138
414 39.000000
415 38.500000
416 29.881138
417 29.881138
Name: Age, Length: 1309, dtype: float64i=0age=[]while i<len(df): if df.iloc[i]['Age']<15: age.append(str(1)) elif df.iloc[i]['Age']<30: age.append(str(2)) elif df.iloc[i]['Age']<45: age.append(str(3)) elif df.iloc[i]['Age']<60: age.append(str(4)) else: age.append(str(5)) i+=1df['age']=agedf=df.drop(['Age'],axis=1)df.head()| Pclass | Sex | Ticket | Fare | Cabin | Embarked | Survived | name | Siblings | Parent | age | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3 | male | A/5 21171 | 7.2500 | NaN | S | 0 | 2 | 1 | 0 | 2 |
| 1 | 1 | female | PC 17599 | 71.2833 | C85 | C | 1 | 1 | 1 | 0 | 3 |
| 2 | 3 | female | STON/O2. 3101282 | 7.9250 | NaN | S | 1 | 3 | 0 | 0 | 2 |
| 3 | 1 | female | 113803 | 53.1000 | C123 | S | 1 | 1 | 1 | 0 | 3 |
| 4 | 3 | male | 373450 | 8.0500 | NaN | S | 0 | 2 | 0 | 0 | 3 |
df['Fare'].fillna(df['Fare'].mean())0 7.2500
1 71.2833
2 7.9250
3 53.1000
4 8.0500
5 8.4583
6 51.8625
7 21.0750
8 11.1333
9 30.0708
10 16.7000
11 26.5500
12 8.0500
13 31.2750
14 7.8542
15 16.0000
16 29.1250
17 13.0000
18 18.0000
19 7.2250
20 26.0000
21 13.0000
22 8.0292
23 35.5000
24 21.0750
25 31.3875
26 7.2250
27 263.0000
28 7.8792
29 7.8958
...
388 7.7500
389 21.0750
390 93.5000
391 39.4000
392 20.2500
393 10.5000
394 22.0250
395 60.0000
396 7.2500
397 79.2000
398 7.7750
399 7.7333
400 164.8667
401 21.0000
402 59.4000
403 47.1000
404 27.7208
405 13.8625
406 10.5000
407 211.5000
408 7.7208
409 13.7750
410 7.7500
411 90.0000
412 7.7750
413 8.0500
414 108.9000
415 7.2500
416 8.0500
417 22.3583
Name: Fare, Length: 1309, dtype: float64i=0fare=[]while i<len(df): if df.iloc[i]['Fare']<15: fare.append(str(1)) elif df.iloc[i]['Fare']<55: fare.append(str(2)) elif df.iloc[i]['Fare']<120: fare.append(str(3)) elif df.iloc[i]['Fare']<190: fare.append(str(4)) else: fare.append(str(5)) i+=1df['fare']=faredf=df.drop(['Fare'],axis=1)df.head()| Pclass | Sex | Ticket | Cabin | Embarked | Survived | name | Siblings | Parent | age | fare | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3 | male | A/5 21171 | NaN | S | 0 | 2 | 1 | 0 | 2 | 1 |
| 1 | 1 | female | PC 17599 | C85 | C | 1 | 1 | 1 | 0 | 3 | 3 |
| 2 | 3 | female | STON/O2. 3101282 | NaN | S | 1 | 3 | 0 | 0 | 2 | 1 |
| 3 | 1 | female | 113803 | C123 | S | 1 | 1 | 1 | 0 | 3 | 2 |
| 4 | 3 | male | 373450 | NaN | S | 0 | 2 | 0 | 0 | 3 | 1 |
i=0ticket=[]while i<len(df): if df.iloc[i]['Ticket'].isnumeric(): ticket.append(str(1)) else: ticket.append(str(0)) i+=1df['ticket']=ticketdf=df.drop(['Ticket'],axis=1)df.head()| Pclass | Sex | Cabin | Embarked | Survived | name | Siblings | Parent | age | fare | ticket | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3 | male | NaN | S | 0 | 2 | 1 | 0 | 2 | 1 | 0 |
| 1 | 1 | female | C85 | C | 1 | 1 | 1 | 0 | 3 | 3 | 0 |
| 2 | 3 | female | NaN | S | 1 | 3 | 0 | 0 | 2 | 1 | 0 |
| 3 | 1 | female | C123 | S | 1 | 1 | 1 | 0 | 3 | 2 | 1 |
| 4 | 3 | male | NaN | S | 0 | 2 | 0 | 0 | 3 | 1 | 1 |
z=0df['Cabin'].fillna("NA")cabin=[]while z<len(df): print(df.iloc[z]['Cabin']) if 'a' in str(df.iloc[z]['Cabin']): cabin.append(str(0)) else: cabin.append(str(1)) z+=1nan C85 nan C123 nan nan E46 nan nan nan G6 C103 nan nan nan nan nan nan nan nan nan D56 nan A6 nan nan nan C23 C25 C27 nan nan nan B78 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan D33 nan B30 C52 nan nan nan nan nan B28 C83 nan nan nan F33 nan nan nan nan nan nan nan nan F G73 nan nan nan nan nan nan nan nan nan nan nan nan C23 C25 C27 nan nan nan E31 nan nan nan A5 D10 D12 nan nan nan nan D26 nan nan nan nan nan nan nan C110 nan nan nan nan nan nan nan B58 B60 nan nan nan nan E101 D26 nan nan nan F E69 nan nan nan nan nan nan nan D47 C123 nan B86 nan nan nan nan nan nan nan nan F2 nan nan C2 nan nan nan nan nan nan nan nan nan nan nan nan nan nan E33 nan nan nan B19 nan nan nan A7 nan nan C49 nan nan nan nan nan F4 nan A32 nan nan nan nan nan nan nan F2 B4 B80 nan nan nan nan nan nan nan nan nan G6 nan nan nan A31 nan nan nan nan nan D36 nan nan D15 nan nan nan nan nan C93 nan nan nan nan nan C83 nan nan nan nan nan nan nan nan nan nan nan nan nan nan C78 nan nan D35 nan nan G6 C87 nan nan nan nan B77 nan nan nan nan E67 B94 nan nan nan nan C125 C99 nan nan nan C118 nan D7 nan nan nan nan nan nan nan nan A19 nan nan nan nan nan nan B49 D nan nan nan nan C22 C26 C106 B58 B60 nan nan nan E101 nan C22 C26 nan C65 nan E36 C54 B57 B59 B63 B66 nan nan nan nan nan nan C7 E34 nan nan nan nan nan C32 nan D nan B18 nan C124 C91 nan nan nan C2 E40 nan T F2 C23 C25 C27 nan nan nan F33 nan nan nan nan nan C128 nan nan nan nan E33 nan nan nan nan nan nan nan nan nan D37 nan nan B35 E50 nan nan nan nan nan nan C82 nan nan nan nan nan nan nan nan nan nan nan nan B96 B98 nan nan D36 G6 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan C78 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan E10 C52 nan nan nan E44 B96 B98 nan nan C23 C25 C27 nan nan nan nan nan nan A34 nan nan nan C104 nan nan C111 C92 nan nan E38 D21 nan nan E12 nan E63 nan nan nan nan nan nan nan nan nan nan D nan A14 nan nan nan nan nan nan nan nan B49 nan C93 B37 nan nan nan nan C30 nan nan nan D20 nan C22 C26 nan nan nan nan nan B79 C65 nan nan nan nan nan nan E25 nan nan D46 F33 nan nan nan B73 nan nan B18 nan nan nan C95 nan nan nan nan nan nan nan nan B38 nan nan B39 B22 nan nan nan C86 nan nan nan nan nan C70 nan nan nan nan nan A16 nan E67 nan nan nan nan nan nan nan nan nan nan nan nan C101 E25 nan nan nan nan E44 nan nan nan C68 nan A10 nan E68 nan B41 nan nan nan D20 nan nan nan nan nan nan nan A20 nan nan nan nan nan nan nan nan nan C125 nan nan nan nan nan nan nan nan F4 nan nan D19 nan nan nan D50 nan D9 nan nan A23 nan B50 nan nan nan nan nan nan nan nan B35 nan nan nan D33 nan A26 nan nan nan nan nan nan nan nan nan nan nan D48 nan nan E58 nan nan nan nan nan nan C126 nan B71 nan nan nan nan nan nan nan B51 B53 B55 nan D49 nan nan nan nan nan nan nan B5 B20 nan nan nan nan nan nan nan C68 F G63 C62 C64 E24 nan nan nan nan nan E24 nan nan C90 C124 C126 nan nan F G73 C45 E101 nan nan nan nan nan nan E8 nan nan nan nan nan B5 nan nan nan nan nan nan B101 nan nan D45 C46 B57 B59 B63 B66 nan nan B22 nan nan D30 nan nan E121 nan nan nan nan nan nan nan B77 nan nan nan B96 B98 nan D11 nan nan nan nan nan nan E77 nan nan nan F38 nan nan B3 nan B20 D6 nan nan nan nan nan nan B82 B84 nan nan nan nan nan nan D17 nan nan nan nan nan B96 B98 nan nan nan A36 nan nan E8 nan nan nan nan nan B102 nan nan nan nan B69 nan nan E121 nan nan nan nan nan B28 nan nan nan nan nan E49 nan nan nan C47 nan nan nan nan nan nan nan nan nan C92 nan nan nan D28 nan nan nan E17 nan nan nan nan D17 nan nan nan nan A24 nan nan nan D35 B51 B53 B55 nan nan nan nan nan nan C50 nan nan nan nan nan nan nan B42 nan C148 nan nan nan nan nan nan nan nan nan nan nan nan nan B45 nan E31 nan nan nan nan nan nan nan nan nan B57 B59 B63 B66 nan B36 nan A21 nan nan nan nan nan C78 nan nan nan nan nan nan D34 nan nan D19 nan A9 nan D15 nan C31 nan nan C23 C25 C27 nan nan nan F G63 nan B61 nan nan nan nan B57 B59 B63 B66 nan nan nan C53 C23 C25 C27 nan nan nan D43 C130 C132 nan C101 nan nan nan C55 C57 nan nan nan nan nan nan nan nan nan nan B71 nan nan nan C46 nan nan nan C116 nan nan nan nan nan nan nan nan F nan nan A29 nan C55 C57 nan nan G6 C6 nan nan nan C28 nan nan nan nan nan nan nan nan C51 nan nan nan nan nan nan nan nan nan nan B57 B59 B63 B66 nan nan nan E46 nan nan nan C54 nan nan nan nan nan C97 nan D22 nan nan nan nan nan nan nan B10 nan nan nan nan nan nan nan nan nan nan C116 F4 E45 nan E52 D30 nan B58 B60 nan nan nan nan nan nan nan nan nan nan nan E34 nan nan nan nan nan C62 C64 nan nan nan nan nan A11 nan nan nan nan nan nan B11 nan nan C80 nan nan nan F33 nan nan nan nan nan nan nan nan nan nan nan C85 nan D37 nan nan C86 nan nan E34 nan nan D21 nan nan nan nan nan nan C89 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan C6 nan C89 nan nan nan nan nan nan nan nan nan nan nan nan nan nan B45 F E46 nan nan nan nan A34 nan nan nan nan nan nan nan D nan nan nan B26 C22 C26 nan B69 nan nan nan nan nan C32 nan B78 nan nan nan nan F E57 F2 nan nan nan F4 nan nan nan nan A18 nan nan nan C106 nan nan nan nan nan nan nan B51 B53 B55 nan nan nan nan nan nan D10 D12 nan nan nan nan E60 C101 nan nan nan nan nan nan nan E50 nan nan nan nan nan nan E39 E41 B52 B54 B56 nan A34 nan nan nan C39 nan nan nan nan nan nan nan nan nan nan nan B24 D28 nan nan nan C31 nan B41 nan nan C7 nan nan nan D40 D38 nan C80 nan nan nan C78 nan nan C105 nan nan nan
df['cabin']=cabindf=df.drop(['Cabin'],axis=1)df.head()| Pclass | Sex | Embarked | Survived | name | Siblings | Parent | age | fare | ticket | cabin | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3 | male | S | 0 | 2 | 1 | 0 | 2 | 1 | 0 | 0 |
| 1 | 1 | female | C | 1 | 1 | 1 | 0 | 3 | 3 | 0 | 1 |
| 2 | 3 | female | S | 1 | 3 | 0 | 0 | 2 | 1 | 0 | 0 |
| 3 | 1 | female | S | 1 | 1 | 1 | 0 | 3 | 2 | 1 | 1 |
| 4 | 3 | male | S | 0 | 2 | 0 | 0 | 3 | 1 | 1 | 0 |
df1=pd.get_dummies(df,columns=['Pclass','Sex',"Embarked",'name','Siblings','Parent','age','fare','ticket','cabin'])df1.head()| Survived | Pclass_1 | Pclass_2 | Pclass_3 | Sex_female | Sex_male | Embarked_C | Embarked_Q | Embarked_S | name_1 | ... | age_5 | fare_1 | fare_2 | fare_3 | fare_4 | fare_5 | ticket_0 | ticket_1 | cabin_0 | cabin_1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 |
| 2 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 3 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 4 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
5 rows × 32 columns
Y=df1['Survived'].valuesX=df1.drop(['Survived'],axis=1)X=X.valuesfrom sklearn.model_selection import train_test_splitX_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.3, random_state=42)import tensorflow as tffrom sklearn.metrics import accuracy_scoredef get_model(): input_layer=tf.keras.layers.Input((31,)) net=tf.keras.layers.Dense(64,'relu')(input_layer) net=tf.keras.layers.Dense(128,'relu')(net) net=tf.keras.layers.Dense(128,'relu')(net) net=tf.keras.layers.Dense(256,'relu')(net) output=tf.keras.layers.Dense(1,'sigmoid')(net) model=tf.keras.Model(inputs=[input_layer],outputs=[output]) return modeldef train_model(X_train,y_train,X_test,y_test): model=get_model() model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy']) history=model.fit(X_train, y_train, batch_size=32, epochs=500, validation_data=(X_test,y_test)) return model,historyclass Neural_Network: def __init__(self,X_train,y_train,X_test,y_test): self.model,self.history=train_model(X_train,y_train,X_test,y_test) def predict_classes(self,X): l=self.model.predict(X) i=0 final=[] while i<len(l): temp=l[i][0] temp_2=float(float(1)-l[i][0]) # print(temp) # print(temp_2) arr=np.array([temp,temp_2],dtype='float32') i+=1 final.append(arr) return np.array(final, dtype='float32') def summary(self): self.model.summary()df_k=df1.drop(['Survived'],axis=1)import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsimport randomfrom xgboost import XGBClassifierfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.ensemble import GradientBoostingClassifierfrom sklearn.metrics import accuracy_score,f1_score#import limeimport lime.lime_tabularprint("a")model_nn=Neural_Network(X_train,Y_train, X_test,Y_test)print("b")classes=['Survived','Not_Survived']all_feat=df_k.columns#model_gb.fit(X_train,Y_train)print(model_nn.predict_classes(X_train).astype(float))predict_fn_nn= lambda x: model_nn.predict_classes(x).astype(float)explainer = lime.lime_tabular.LimeTabularExplainer(X_train,mode='classification',feature_selection= 'auto', class_names=classes,feature_names = all_feat, kernel_width=None,discretize_continuous=True)observation=55exp = explainer.explain_instance(X_test[observation_1], predict_fn_nn, num_features=1,top_labels=1)a Train on 916 samples, validate on 393 samples Epoch 1/500 916/916 [==============================] - 0s 227us/sample - loss: 0.4987 - acc: 0.7358 - val_loss: 0.3983 - val_acc: 0.8524 Epoch 2/500 916/916 [==============================] - 0s 66us/sample - loss: 0.3750 - acc: 0.8537 - val_loss: 0.3721 - val_acc: 0.8575 Epoch 3/500 916/916 [==============================] - 0s 60us/sample - loss: 0.3643 - acc: 0.8493 - val_loss: 0.3841 - val_acc: 0.8524 Epoch 4/500 916/916 [==============================] - 0s 61us/sample - loss: 0.3532 - acc: 0.8559 - val_loss: 0.3586 - val_acc: 0.8601 Epoch 5/500 916/916 [==============================] - 0s 58us/sample - loss: 0.3357 - acc: 0.8723 - val_loss: 0.3690 - val_acc: 0.8524 Epoch 6/500 916/916 [==============================] - 0s 67us/sample - loss: 0.3231 - acc: 0.8777 - val_loss: 0.3697 - val_acc: 0.8601 Epoch 7/500 916/916 [==============================] - 0s 60us/sample - loss: 0.3224 - acc: 0.8788 - val_loss: 0.3637 - val_acc: 0.8626 Epoch 8/500 916/916 [==============================] - 0s 64us/sample - loss: 0.3084 - acc: 0.8854 - val_loss: 0.3752 - val_acc: 0.8626 Epoch 9/500 916/916 [==============================] - 0s 64us/sample - loss: 0.3167 - acc: 0.8766 - val_loss: 0.3983 - val_acc: 0.8448 Epoch 10/500 916/916 [==============================] - 0s 92us/sample - loss: 0.2870 - acc: 0.8897 - val_loss: 0.4014 - val_acc: 0.8550 Epoch 11/500 916/916 [==============================] - 0s 77us/sample - loss: 0.2865 - acc: 0.8941 - val_loss: 0.3937 - val_acc: 0.8499 Epoch 12/500 916/916 [==============================] - 0s 75us/sample - loss: 0.2733 - acc: 0.8919 - val_loss: 0.4223 - val_acc: 0.8499 Epoch 13/500 916/916 [==============================] - 0s 84us/sample - loss: 0.2737 - acc: 0.8908 - val_loss: 0.4251 - val_acc: 0.8499 Epoch 14/500 916/916 [==============================] - 0s 84us/sample - loss: 0.2664 - acc: 0.9007 - val_loss: 0.4333 - val_acc: 0.8499 Epoch 15/500 916/916 [==============================] - 0s 77us/sample - loss: 0.2541 - acc: 0.9061 - val_loss: 0.4887 - val_acc: 0.8422 Epoch 16/500 916/916 [==============================] - 0s 69us/sample - loss: 0.2527 - acc: 0.9017 - val_loss: 0.4682 - val_acc: 0.8473 Epoch 17/500 916/916 [==============================] - 0s 71us/sample - loss: 0.2316 - acc: 0.9094 - val_loss: 0.4812 - val_acc: 0.8473 Epoch 18/500 916/916 [==============================] - 0s 68us/sample - loss: 0.2317 - acc: 0.9170 - val_loss: 0.5029 - val_acc: 0.8397 Epoch 19/500 916/916 [==============================] - 0s 61us/sample - loss: 0.2210 - acc: 0.9127 - val_loss: 0.5336 - val_acc: 0.8372 Epoch 20/500 916/916 [==============================] - 0s 59us/sample - loss: 0.2312 - acc: 0.9148 - val_loss: 0.5017 - val_acc: 0.8346 Epoch 21/500 916/916 [==============================] - 0s 68us/sample - loss: 0.2226 - acc: 0.9170 - val_loss: 0.5824 - val_acc: 0.8422 Epoch 22/500 916/916 [==============================] - 0s 63us/sample - loss: 0.2304 - acc: 0.9148 - val_loss: 0.5455 - val_acc: 0.8448 Epoch 23/500 916/916 [==============================] - 0s 58us/sample - loss: 0.2192 - acc: 0.9192 - val_loss: 0.5632 - val_acc: 0.8346 Epoch 24/500 916/916 [==============================] - 0s 57us/sample - loss: 0.2142 - acc: 0.9192 - val_loss: 0.5681 - val_acc: 0.8422 Epoch 25/500 916/916 [==============================] - 0s 63us/sample - loss: 0.2147 - acc: 0.9203 - val_loss: 0.5964 - val_acc: 0.8448 Epoch 26/500 916/916 [==============================] - 0s 57us/sample - loss: 0.2124 - acc: 0.9105 - val_loss: 0.6149 - val_acc: 0.8372 Epoch 27/500 916/916 [==============================] - 0s 59us/sample - loss: 0.2029 - acc: 0.9214 - val_loss: 0.6404 - val_acc: 0.8372 Epoch 28/500 916/916 [==============================] - 0s 59us/sample - loss: 0.2133 - acc: 0.9138 - val_loss: 0.6062 - val_acc: 0.8270 Epoch 29/500 916/916 [==============================] - 0s 60us/sample - loss: 0.2257 - acc: 0.9138 - val_loss: 0.6351 - val_acc: 0.8372 Epoch 30/500 916/916 [==============================] - 0s 96us/sample - loss: 0.2250 - acc: 0.9127 - val_loss: 0.6886 - val_acc: 0.8422 Epoch 31/500 916/916 [==============================] - 0s 66us/sample - loss: 0.2093 - acc: 0.9170 - val_loss: 0.6607 - val_acc: 0.8346 Epoch 32/500 916/916 [==============================] - 0s 68us/sample - loss: 0.2018 - acc: 0.9192 - val_loss: 0.7038 - val_acc: 0.8397 Epoch 33/500 916/916 [==============================] - 0s 67us/sample - loss: 0.2001 - acc: 0.9214 - val_loss: 0.7017 - val_acc: 0.8372 Epoch 34/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1986 - acc: 0.9192 - val_loss: 0.7194 - val_acc: 0.8372 Epoch 35/500 916/916 [==============================] - 0s 63us/sample - loss: 0.2000 - acc: 0.9203 - val_loss: 0.7095 - val_acc: 0.8422 Epoch 36/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1974 - acc: 0.9159 - val_loss: 0.7384 - val_acc: 0.8372 Epoch 37/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1951 - acc: 0.9214 - val_loss: 0.7674 - val_acc: 0.8397 Epoch 38/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1960 - acc: 0.9192 - val_loss: 0.7783 - val_acc: 0.8397 Epoch 39/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1987 - acc: 0.9214 - val_loss: 0.7678 - val_acc: 0.8372 Epoch 40/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1969 - acc: 0.9192 - val_loss: 0.8118 - val_acc: 0.8422 Epoch 41/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1925 - acc: 0.9214 - val_loss: 0.7939 - val_acc: 0.8372 Epoch 42/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1971 - acc: 0.9159 - val_loss: 0.7742 - val_acc: 0.8372 Epoch 43/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1938 - acc: 0.9192 - val_loss: 0.8419 - val_acc: 0.8346 Epoch 44/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1897 - acc: 0.9170 - val_loss: 0.9028 - val_acc: 0.8397 Epoch 45/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1943 - acc: 0.9225 - val_loss: 0.8314 - val_acc: 0.8397 Epoch 46/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1942 - acc: 0.9181 - val_loss: 0.9114 - val_acc: 0.8372 Epoch 47/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1916 - acc: 0.9192 - val_loss: 0.9218 - val_acc: 0.8397 Epoch 48/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1887 - acc: 0.9170 - val_loss: 0.8996 - val_acc: 0.8346 Epoch 49/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1851 - acc: 0.9214 - val_loss: 0.9628 - val_acc: 0.8372 Epoch 50/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1924 - acc: 0.9225 - val_loss: 0.9504 - val_acc: 0.8321 Epoch 51/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1873 - acc: 0.9192 - val_loss: 0.9402 - val_acc: 0.8397 Epoch 52/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1882 - acc: 0.9170 - val_loss: 0.9646 - val_acc: 0.8397 Epoch 53/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1888 - acc: 0.9192 - val_loss: 1.0224 - val_acc: 0.8346 Epoch 54/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1924 - acc: 0.9192 - val_loss: 0.9067 - val_acc: 0.8397 Epoch 55/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1906 - acc: 0.9192 - val_loss: 1.0035 - val_acc: 0.8448 Epoch 56/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1861 - acc: 0.9225 - val_loss: 0.9833 - val_acc: 0.8422 Epoch 57/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1831 - acc: 0.9203 - val_loss: 1.0479 - val_acc: 0.8397 Epoch 58/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1859 - acc: 0.9236 - val_loss: 1.1254 - val_acc: 0.8397 Epoch 59/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1885 - acc: 0.9170 - val_loss: 1.0231 - val_acc: 0.8422 Epoch 60/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1857 - acc: 0.9225 - val_loss: 1.0673 - val_acc: 0.8422 Epoch 61/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1869 - acc: 0.9214 - val_loss: 1.1115 - val_acc: 0.8372 Epoch 62/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1851 - acc: 0.9203 - val_loss: 1.0929 - val_acc: 0.8422 Epoch 63/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1843 - acc: 0.9192 - val_loss: 1.1671 - val_acc: 0.8372 Epoch 64/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1841 - acc: 0.9225 - val_loss: 1.1188 - val_acc: 0.8448 Epoch 65/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1805 - acc: 0.9203 - val_loss: 1.1780 - val_acc: 0.8422 Epoch 66/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1820 - acc: 0.9225 - val_loss: 1.2009 - val_acc: 0.8397 Epoch 67/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1863 - acc: 0.9170 - val_loss: 1.1925 - val_acc: 0.8397 Epoch 68/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1841 - acc: 0.9225 - val_loss: 1.1597 - val_acc: 0.8346 Epoch 69/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1837 - acc: 0.9214 - val_loss: 1.1928 - val_acc: 0.8397 Epoch 70/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1806 - acc: 0.9203 - val_loss: 1.2274 - val_acc: 0.8372 Epoch 71/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1828 - acc: 0.9203 - val_loss: 1.2832 - val_acc: 0.8372 Epoch 72/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1826 - acc: 0.9192 - val_loss: 1.2728 - val_acc: 0.8321 Epoch 73/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1880 - acc: 0.9203 - val_loss: 1.1978 - val_acc: 0.8372 Epoch 74/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1816 - acc: 0.9225 - val_loss: 1.2904 - val_acc: 0.8422 Epoch 75/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1812 - acc: 0.9192 - val_loss: 1.3182 - val_acc: 0.8397 Epoch 76/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1804 - acc: 0.9214 - val_loss: 1.2342 - val_acc: 0.8397 Epoch 77/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1802 - acc: 0.9214 - val_loss: 1.3220 - val_acc: 0.8397 Epoch 78/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1819 - acc: 0.9203 - val_loss: 1.2963 - val_acc: 0.8422 Epoch 79/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1784 - acc: 0.9214 - val_loss: 1.3232 - val_acc: 0.8346 Epoch 80/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1817 - acc: 0.9236 - val_loss: 1.3150 - val_acc: 0.8422 Epoch 81/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1836 - acc: 0.9170 - val_loss: 1.3065 - val_acc: 0.8321 Epoch 82/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1811 - acc: 0.9225 - val_loss: 1.3495 - val_acc: 0.8372 Epoch 83/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1812 - acc: 0.9214 - val_loss: 1.3536 - val_acc: 0.8346 Epoch 84/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1804 - acc: 0.9214 - val_loss: 1.3983 - val_acc: 0.8346 Epoch 85/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1785 - acc: 0.9181 - val_loss: 1.4485 - val_acc: 0.8422 Epoch 86/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1804 - acc: 0.9192 - val_loss: 1.4225 - val_acc: 0.8346 Epoch 87/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1792 - acc: 0.9192 - val_loss: 1.4440 - val_acc: 0.8372 Epoch 88/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1796 - acc: 0.9170 - val_loss: 1.4770 - val_acc: 0.8346 Epoch 89/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1798 - acc: 0.9181 - val_loss: 1.4580 - val_acc: 0.8372 Epoch 90/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1800 - acc: 0.9214 - val_loss: 1.4333 - val_acc: 0.8397 Epoch 91/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1812 - acc: 0.9203 - val_loss: 1.5171 - val_acc: 0.8321 Epoch 92/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1798 - acc: 0.9192 - val_loss: 1.4751 - val_acc: 0.8422 Epoch 93/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1786 - acc: 0.9192 - val_loss: 1.4967 - val_acc: 0.8346 Epoch 94/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1786 - acc: 0.9192 - val_loss: 1.4455 - val_acc: 0.8372 Epoch 95/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1801 - acc: 0.9225 - val_loss: 1.4813 - val_acc: 0.8346 Epoch 96/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1779 - acc: 0.9192 - val_loss: 1.5344 - val_acc: 0.8422 Epoch 97/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1777 - acc: 0.9192 - val_loss: 1.5393 - val_acc: 0.8346 Epoch 98/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1805 - acc: 0.9181 - val_loss: 1.5171 - val_acc: 0.8372 Epoch 99/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1782 - acc: 0.9203 - val_loss: 1.5545 - val_acc: 0.8346 Epoch 100/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1781 - acc: 0.9225 - val_loss: 1.5543 - val_acc: 0.8346 Epoch 101/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1784 - acc: 0.9203 - val_loss: 1.5724 - val_acc: 0.8346 Epoch 102/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1795 - acc: 0.9203 - val_loss: 1.5280 - val_acc: 0.8346 Epoch 103/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1776 - acc: 0.9170 - val_loss: 1.5268 - val_acc: 0.8372 Epoch 104/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1780 - acc: 0.9214 - val_loss: 1.5804 - val_acc: 0.8346 Epoch 105/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1808 - acc: 0.9181 - val_loss: 1.6256 - val_acc: 0.8397 Epoch 106/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1770 - acc: 0.9214 - val_loss: 1.5635 - val_acc: 0.8346 Epoch 107/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1791 - acc: 0.9225 - val_loss: 1.5941 - val_acc: 0.8372 Epoch 108/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1786 - acc: 0.9203 - val_loss: 1.5985 - val_acc: 0.8372 Epoch 109/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1791 - acc: 0.9225 - val_loss: 1.6251 - val_acc: 0.8321 Epoch 110/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1807 - acc: 0.9225 - val_loss: 1.6269 - val_acc: 0.8346 Epoch 111/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1796 - acc: 0.9225 - val_loss: 1.6133 - val_acc: 0.8372 Epoch 112/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1809 - acc: 0.9214 - val_loss: 1.6212 - val_acc: 0.8346 Epoch 113/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1829 - acc: 0.9170 - val_loss: 1.6324 - val_acc: 0.8372 Epoch 114/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1813 - acc: 0.9170 - val_loss: 1.6349 - val_acc: 0.8346 Epoch 115/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1782 - acc: 0.9181 - val_loss: 1.6584 - val_acc: 0.8346 Epoch 116/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1821 - acc: 0.9236 - val_loss: 1.6410 - val_acc: 0.8397 Epoch 117/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1794 - acc: 0.9181 - val_loss: 1.6309 - val_acc: 0.8346 Epoch 118/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1785 - acc: 0.9192 - val_loss: 1.6642 - val_acc: 0.8372 Epoch 119/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1782 - acc: 0.9203 - val_loss: 1.6980 - val_acc: 0.8372
Epoch 120/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1796 - acc: 0.9192 - val_loss: 1.6651 - val_acc: 0.8346 Epoch 121/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1779 - acc: 0.9214 - val_loss: 1.6547 - val_acc: 0.8346 Epoch 122/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1784 - acc: 0.9214 - val_loss: 1.6666 - val_acc: 0.8346 Epoch 123/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1796 - acc: 0.9214 - val_loss: 1.6735 - val_acc: 0.8346 Epoch 124/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1785 - acc: 0.9225 - val_loss: 1.6958 - val_acc: 0.8397 Epoch 125/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1764 - acc: 0.9225 - val_loss: 1.7062 - val_acc: 0.8397 Epoch 126/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1772 - acc: 0.9225 - val_loss: 1.7303 - val_acc: 0.8321 Epoch 127/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1777 - acc: 0.9214 - val_loss: 1.7145 - val_acc: 0.8372 Epoch 128/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1775 - acc: 0.9203 - val_loss: 1.6897 - val_acc: 0.8397 Epoch 129/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1769 - acc: 0.9214 - val_loss: 1.7649 - val_acc: 0.8346 Epoch 130/500 916/916 [==============================] - 0s 51us/sample - loss: 0.1787 - acc: 0.9192 - val_loss: 1.7336 - val_acc: 0.8397 Epoch 131/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1782 - acc: 0.9181 - val_loss: 1.7260 - val_acc: 0.8346 Epoch 132/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1768 - acc: 0.9203 - val_loss: 1.6975 - val_acc: 0.8346 Epoch 133/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1776 - acc: 0.9203 - val_loss: 1.7372 - val_acc: 0.8321 Epoch 134/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1795 - acc: 0.9181 - val_loss: 1.7614 - val_acc: 0.8321 Epoch 135/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1778 - acc: 0.9225 - val_loss: 1.7306 - val_acc: 0.8346 Epoch 136/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1793 - acc: 0.9192 - val_loss: 1.7722 - val_acc: 0.8372 Epoch 137/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1795 - acc: 0.9225 - val_loss: 1.7670 - val_acc: 0.8346 Epoch 138/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1781 - acc: 0.9181 - val_loss: 1.7503 - val_acc: 0.8372 Epoch 139/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1778 - acc: 0.9225 - val_loss: 1.7585 - val_acc: 0.8397 Epoch 140/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1764 - acc: 0.9236 - val_loss: 1.7920 - val_acc: 0.8397 Epoch 141/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1787 - acc: 0.9203 - val_loss: 1.8057 - val_acc: 0.8346 Epoch 142/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1775 - acc: 0.9203 - val_loss: 1.8463 - val_acc: 0.8321 Epoch 143/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1783 - acc: 0.9225 - val_loss: 1.7833 - val_acc: 0.8346 Epoch 144/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1801 - acc: 0.9236 - val_loss: 1.7382 - val_acc: 0.8270 Epoch 145/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1779 - acc: 0.9236 - val_loss: 1.7641 - val_acc: 0.8346 Epoch 146/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1791 - acc: 0.9203 - val_loss: 1.7481 - val_acc: 0.8346 Epoch 147/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1780 - acc: 0.9192 - val_loss: 1.8103 - val_acc: 0.8372 Epoch 148/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1778 - acc: 0.9192 - val_loss: 1.8536 - val_acc: 0.8372 Epoch 149/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1773 - acc: 0.9203 - val_loss: 1.8641 - val_acc: 0.8321 Epoch 150/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1766 - acc: 0.9225 - val_loss: 1.9047 - val_acc: 0.8346 Epoch 151/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1783 - acc: 0.9181 - val_loss: 1.8779 - val_acc: 0.8372 Epoch 152/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1778 - acc: 0.9192 - val_loss: 1.8432 - val_acc: 0.8321 Epoch 153/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1778 - acc: 0.9225 - val_loss: 1.8667 - val_acc: 0.8321 Epoch 154/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1777 - acc: 0.9203 - val_loss: 1.8718 - val_acc: 0.8346 Epoch 155/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1769 - acc: 0.9214 - val_loss: 1.8895 - val_acc: 0.8295 Epoch 156/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1768 - acc: 0.9192 - val_loss: 1.8654 - val_acc: 0.8372 Epoch 157/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1770 - acc: 0.9203 - val_loss: 1.8709 - val_acc: 0.8321 Epoch 158/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1809 - acc: 0.9225 - val_loss: 1.9070 - val_acc: 0.8372 Epoch 159/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1782 - acc: 0.9214 - val_loss: 1.8555 - val_acc: 0.8346 Epoch 160/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1774 - acc: 0.9214 - val_loss: 1.8726 - val_acc: 0.8346 Epoch 161/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1772 - acc: 0.9170 - val_loss: 1.8821 - val_acc: 0.8321 Epoch 162/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1777 - acc: 0.9247 - val_loss: 1.8537 - val_acc: 0.8372 Epoch 163/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1774 - acc: 0.9192 - val_loss: 1.9278 - val_acc: 0.8346 Epoch 164/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1780 - acc: 0.9170 - val_loss: 1.9116 - val_acc: 0.8372 Epoch 165/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1776 - acc: 0.9214 - val_loss: 1.9386 - val_acc: 0.8295 Epoch 166/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1773 - acc: 0.9203 - val_loss: 1.8549 - val_acc: 0.8346 Epoch 167/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1763 - acc: 0.9203 - val_loss: 1.9406 - val_acc: 0.8346 Epoch 168/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1777 - acc: 0.9225 - val_loss: 1.9635 - val_acc: 0.8321 Epoch 169/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1763 - acc: 0.9225 - val_loss: 1.9414 - val_acc: 0.8372 Epoch 170/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1760 - acc: 0.9214 - val_loss: 1.9512 - val_acc: 0.8372 Epoch 171/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1767 - acc: 0.9214 - val_loss: 1.9806 - val_acc: 0.8321 Epoch 172/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1756 - acc: 0.9203 - val_loss: 1.9518 - val_acc: 0.8397 Epoch 173/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1775 - acc: 0.9159 - val_loss: 1.9769 - val_acc: 0.8372 Epoch 174/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1773 - acc: 0.9225 - val_loss: 1.9704 - val_acc: 0.8346 Epoch 175/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1772 - acc: 0.9192 - val_loss: 1.8867 - val_acc: 0.8346 Epoch 176/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1772 - acc: 0.9214 - val_loss: 1.9359 - val_acc: 0.8321 Epoch 177/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1770 - acc: 0.9225 - val_loss: 1.9678 - val_acc: 0.8372 Epoch 178/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1771 - acc: 0.9236 - val_loss: 2.0283 - val_acc: 0.8346 Epoch 179/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1767 - acc: 0.9192 - val_loss: 2.0703 - val_acc: 0.8397 Epoch 180/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1772 - acc: 0.9203 - val_loss: 1.9604 - val_acc: 0.8346 Epoch 181/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1768 - acc: 0.9247 - val_loss: 1.9692 - val_acc: 0.8321 Epoch 182/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1767 - acc: 0.9192 - val_loss: 2.0234 - val_acc: 0.8422 Epoch 183/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1758 - acc: 0.9181 - val_loss: 2.0178 - val_acc: 0.8372 Epoch 184/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1771 - acc: 0.9192 - val_loss: 2.0147 - val_acc: 0.8321 Epoch 185/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1770 - acc: 0.9236 - val_loss: 2.0381 - val_acc: 0.8321 Epoch 186/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1789 - acc: 0.9214 - val_loss: 2.0447 - val_acc: 0.8346 Epoch 187/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1782 - acc: 0.9236 - val_loss: 1.9570 - val_acc: 0.8372 Epoch 188/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1767 - acc: 0.9203 - val_loss: 1.9471 - val_acc: 0.8372 Epoch 189/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1766 - acc: 0.9181 - val_loss: 1.9850 - val_acc: 0.8346 Epoch 190/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1772 - acc: 0.9192 - val_loss: 2.0507 - val_acc: 0.8321 Epoch 191/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1796 - acc: 0.9170 - val_loss: 2.0271 - val_acc: 0.8346 Epoch 192/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1772 - acc: 0.9225 - val_loss: 2.0051 - val_acc: 0.8372 Epoch 193/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1770 - acc: 0.9236 - val_loss: 2.0311 - val_acc: 0.8397 Epoch 194/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1768 - acc: 0.9214 - val_loss: 2.0654 - val_acc: 0.8346 Epoch 195/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1767 - acc: 0.9225 - val_loss: 2.0190 - val_acc: 0.8346 Epoch 196/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1779 - acc: 0.9203 - val_loss: 2.0891 - val_acc: 0.8346 Epoch 197/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1810 - acc: 0.9214 - val_loss: 2.0245 - val_acc: 0.8321 Epoch 198/500 916/916 [==============================] - 0s 57us/sample - loss: 0.2224 - acc: 0.9159 - val_loss: 1.3593 - val_acc: 0.8168 Epoch 199/500 916/916 [==============================] - 0s 57us/sample - loss: 0.2922 - acc: 0.8908 - val_loss: 0.7853 - val_acc: 0.8397 Epoch 200/500 916/916 [==============================] - 0s 60us/sample - loss: 0.2979 - acc: 0.8930 - val_loss: 0.5268 - val_acc: 0.8422 Epoch 201/500 916/916 [==============================] - 0s 54us/sample - loss: 0.2454 - acc: 0.8963 - val_loss: 0.6948 - val_acc: 0.8270 Epoch 202/500 916/916 [==============================] - 0s 56us/sample - loss: 0.2205 - acc: 0.9192 - val_loss: 0.8388 - val_acc: 0.8397 Epoch 203/500 916/916 [==============================] - 0s 63us/sample - loss: 0.2035 - acc: 0.9159 - val_loss: 0.9899 - val_acc: 0.8372 Epoch 204/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1931 - acc: 0.9192 - val_loss: 1.1888 - val_acc: 0.8422 Epoch 205/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1908 - acc: 0.9203 - val_loss: 1.2164 - val_acc: 0.8372 Epoch 206/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1879 - acc: 0.9181 - val_loss: 1.2471 - val_acc: 0.8346 Epoch 207/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1867 - acc: 0.9181 - val_loss: 1.2943 - val_acc: 0.8473 Epoch 208/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1844 - acc: 0.9236 - val_loss: 1.4018 - val_acc: 0.8346 Epoch 209/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1858 - acc: 0.9192 - val_loss: 1.3298 - val_acc: 0.8372 Epoch 210/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1825 - acc: 0.9203 - val_loss: 1.4070 - val_acc: 0.8448 Epoch 211/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1801 - acc: 0.9225 - val_loss: 1.4337 - val_acc: 0.8397 Epoch 212/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1803 - acc: 0.9214 - val_loss: 1.5169 - val_acc: 0.8448 Epoch 213/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1803 - acc: 0.9225 - val_loss: 1.5417 - val_acc: 0.8397 Epoch 214/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1805 - acc: 0.9236 - val_loss: 1.4982 - val_acc: 0.8448 Epoch 215/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1818 - acc: 0.9203 - val_loss: 1.5607 - val_acc: 0.8448 Epoch 216/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1833 - acc: 0.9192 - val_loss: 1.5185 - val_acc: 0.8372 Epoch 217/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1798 - acc: 0.9236 - val_loss: 1.5545 - val_acc: 0.8397 Epoch 218/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1789 - acc: 0.9192 - val_loss: 1.6842 - val_acc: 0.8422 Epoch 219/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1765 - acc: 0.9214 - val_loss: 1.6717 - val_acc: 0.8372 Epoch 220/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1772 - acc: 0.9225 - val_loss: 1.7039 - val_acc: 0.8372 Epoch 221/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1772 - acc: 0.9170 - val_loss: 1.7275 - val_acc: 0.8397 Epoch 222/500 916/916 [==============================] - 0s 77us/sample - loss: 0.1776 - acc: 0.9159 - val_loss: 1.7404 - val_acc: 0.8397 Epoch 223/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1779 - acc: 0.9203 - val_loss: 1.7186 - val_acc: 0.8372 Epoch 224/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1763 - acc: 0.9203 - val_loss: 1.7291 - val_acc: 0.8422 Epoch 225/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1771 - acc: 0.9192 - val_loss: 1.7342 - val_acc: 0.8397 Epoch 226/500 916/916 [==============================] - 0s 72us/sample - loss: 0.1770 - acc: 0.9181 - val_loss: 1.7381 - val_acc: 0.8397 Epoch 227/500 916/916 [==============================] - 0s 70us/sample - loss: 0.1764 - acc: 0.9203 - val_loss: 1.7794 - val_acc: 0.8372 Epoch 228/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1775 - acc: 0.9192 - val_loss: 1.6702 - val_acc: 0.8499 Epoch 229/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1786 - acc: 0.9203 - val_loss: 1.7317 - val_acc: 0.8473 Epoch 230/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1763 - acc: 0.9225 - val_loss: 1.7066 - val_acc: 0.8448 Epoch 231/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1762 - acc: 0.9214 - val_loss: 1.7361 - val_acc: 0.8397 Epoch 232/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1761 - acc: 0.9214 - val_loss: 1.7823 - val_acc: 0.8397 Epoch 233/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1773 - acc: 0.9225 - val_loss: 1.8281 - val_acc: 0.8372 Epoch 234/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1764 - acc: 0.9203 - val_loss: 1.7786 - val_acc: 0.8372 Epoch 235/500 916/916 [==============================] - 0s 51us/sample - loss: 0.1773 - acc: 0.9225 - val_loss: 1.7938 - val_acc: 0.8372 Epoch 236/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1765 - acc: 0.9236 - val_loss: 1.7301 - val_acc: 0.8448 Epoch 237/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1759 - acc: 0.9225 - val_loss: 1.8240 - val_acc: 0.8397 Epoch 238/500
916/916 [==============================] - 0s 55us/sample - loss: 0.1760 - acc: 0.9214 - val_loss: 1.8766 - val_acc: 0.8397 Epoch 239/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1761 - acc: 0.9203 - val_loss: 1.8369 - val_acc: 0.8422 Epoch 240/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1770 - acc: 0.9203 - val_loss: 1.8267 - val_acc: 0.8448 Epoch 241/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1758 - acc: 0.9203 - val_loss: 1.8424 - val_acc: 0.8422 Epoch 242/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1771 - acc: 0.9192 - val_loss: 1.8645 - val_acc: 0.8372 Epoch 243/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1777 - acc: 0.9170 - val_loss: 1.8824 - val_acc: 0.8346 Epoch 244/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1759 - acc: 0.9170 - val_loss: 1.8874 - val_acc: 0.8397 Epoch 245/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1761 - acc: 0.9236 - val_loss: 1.8646 - val_acc: 0.8422 Epoch 246/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1751 - acc: 0.9192 - val_loss: 1.8687 - val_acc: 0.8422 Epoch 247/500 916/916 [==============================] - 0s 51us/sample - loss: 0.1765 - acc: 0.9225 - val_loss: 1.8586 - val_acc: 0.8397 Epoch 248/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1765 - acc: 0.9225 - val_loss: 1.8895 - val_acc: 0.8422 Epoch 249/500 916/916 [==============================] - 0s 101us/sample - loss: 0.1758 - acc: 0.9225 - val_loss: 1.8800 - val_acc: 0.8372 Epoch 250/500 916/916 [==============================] - 0s 78us/sample - loss: 0.1758 - acc: 0.9181 - val_loss: 1.8837 - val_acc: 0.8372 Epoch 251/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1757 - acc: 0.9236 - val_loss: 1.9088 - val_acc: 0.8397 Epoch 252/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1768 - acc: 0.9236 - val_loss: 1.9276 - val_acc: 0.8397 Epoch 253/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1755 - acc: 0.9225 - val_loss: 1.8964 - val_acc: 0.8422 Epoch 254/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1758 - acc: 0.9203 - val_loss: 1.9229 - val_acc: 0.8372 Epoch 255/500 916/916 [==============================] - 0s 49us/sample - loss: 0.1762 - acc: 0.9192 - val_loss: 1.9504 - val_acc: 0.8397 Epoch 256/500 916/916 [==============================] - 0s 49us/sample - loss: 0.1765 - acc: 0.9192 - val_loss: 1.9115 - val_acc: 0.8422 Epoch 257/500 916/916 [==============================] - 0s 51us/sample - loss: 0.1764 - acc: 0.9203 - val_loss: 1.9033 - val_acc: 0.8397 Epoch 258/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1782 - acc: 0.9236 - val_loss: 1.8989 - val_acc: 0.8397 Epoch 259/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1766 - acc: 0.9236 - val_loss: 1.8999 - val_acc: 0.8372 Epoch 260/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1759 - acc: 0.9170 - val_loss: 1.9249 - val_acc: 0.8397 Epoch 261/500 916/916 [==============================] - 0s 82us/sample - loss: 0.1754 - acc: 0.9192 - val_loss: 1.9061 - val_acc: 0.8397 Epoch 262/500 916/916 [==============================] - 0s 73us/sample - loss: 0.1763 - acc: 0.9203 - val_loss: 1.9505 - val_acc: 0.8346 Epoch 263/500 916/916 [==============================] - 0s 43us/sample - loss: 0.1770 - acc: 0.9192 - val_loss: 1.9164 - val_acc: 0.8448 Epoch 264/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1762 - acc: 0.9192 - val_loss: 1.9083 - val_acc: 0.8397 Epoch 265/500 916/916 [==============================] - 0s 89us/sample - loss: 0.1762 - acc: 0.9236 - val_loss: 1.9216 - val_acc: 0.8372 Epoch 266/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1762 - acc: 0.9203 - val_loss: 1.9219 - val_acc: 0.8372 Epoch 267/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1758 - acc: 0.9236 - val_loss: 1.9580 - val_acc: 0.8397 Epoch 268/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1765 - acc: 0.9192 - val_loss: 1.9411 - val_acc: 0.8346 Epoch 269/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1767 - acc: 0.9214 - val_loss: 1.9177 - val_acc: 0.8372 Epoch 270/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1763 - acc: 0.9236 - val_loss: 1.9242 - val_acc: 0.8346 Epoch 271/500 916/916 [==============================] - 0s 75us/sample - loss: 0.1770 - acc: 0.9192 - val_loss: 1.9374 - val_acc: 0.8422 Epoch 272/500 916/916 [==============================] - 0s 91us/sample - loss: 0.1764 - acc: 0.9170 - val_loss: 1.9357 - val_acc: 0.8397 Epoch 273/500 916/916 [==============================] - 0s 76us/sample - loss: 0.1755 - acc: 0.9214 - val_loss: 1.9543 - val_acc: 0.8422 Epoch 274/500 916/916 [==============================] - 0s 48us/sample - loss: 0.1761 - acc: 0.9203 - val_loss: 1.9510 - val_acc: 0.8448 Epoch 275/500 916/916 [==============================] - 0s 47us/sample - loss: 0.1756 - acc: 0.9181 - val_loss: 1.9766 - val_acc: 0.8372 Epoch 276/500 916/916 [==============================] - 0s 47us/sample - loss: 0.1760 - acc: 0.9159 - val_loss: 1.9788 - val_acc: 0.8397 Epoch 277/500 916/916 [==============================] - 0s 48us/sample - loss: 0.1767 - acc: 0.9236 - val_loss: 1.9638 - val_acc: 0.8372 Epoch 278/500 916/916 [==============================] - 0s 47us/sample - loss: 0.1766 - acc: 0.9203 - val_loss: 1.9718 - val_acc: 0.8422 Epoch 279/500 916/916 [==============================] - 0s 46us/sample - loss: 0.1761 - acc: 0.9225 - val_loss: 1.9298 - val_acc: 0.8422 Epoch 280/500 916/916 [==============================] - 0s 44us/sample - loss: 0.1747 - acc: 0.9214 - val_loss: 1.9367 - val_acc: 0.8422 Epoch 281/500 916/916 [==============================] - 0s 43us/sample - loss: 0.1756 - acc: 0.9170 - val_loss: 1.9828 - val_acc: 0.8397 Epoch 282/500 916/916 [==============================] - 0s 43us/sample - loss: 0.1768 - acc: 0.9181 - val_loss: 1.9507 - val_acc: 0.8422 Epoch 283/500 916/916 [==============================] - 0s 43us/sample - loss: 0.1754 - acc: 0.9192 - val_loss: 1.9678 - val_acc: 0.8372 Epoch 284/500 916/916 [==============================] - 0s 44us/sample - loss: 0.1767 - acc: 0.9214 - val_loss: 1.9415 - val_acc: 0.8448 Epoch 285/500 916/916 [==============================] - 0s 44us/sample - loss: 0.1784 - acc: 0.9203 - val_loss: 1.9414 - val_acc: 0.8448 Epoch 286/500 916/916 [==============================] - 0s 45us/sample - loss: 0.1757 - acc: 0.9214 - val_loss: 1.9678 - val_acc: 0.8448 Epoch 287/500 916/916 [==============================] - 0s 44us/sample - loss: 0.1757 - acc: 0.9192 - val_loss: 1.9767 - val_acc: 0.8372 Epoch 288/500 916/916 [==============================] - 0s 107us/sample - loss: 0.1755 - acc: 0.9225 - val_loss: 1.9465 - val_acc: 0.8448 Epoch 289/500 916/916 [==============================] - 0s 69us/sample - loss: 0.1753 - acc: 0.9214 - val_loss: 1.9612 - val_acc: 0.8397 Epoch 290/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1757 - acc: 0.9214 - val_loss: 1.9702 - val_acc: 0.8397 Epoch 291/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1759 - acc: 0.9192 - val_loss: 1.9975 - val_acc: 0.8397 Epoch 292/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1760 - acc: 0.9192 - val_loss: 1.9759 - val_acc: 0.8397 Epoch 293/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1758 - acc: 0.9214 - val_loss: 1.9850 - val_acc: 0.8422 Epoch 294/500 916/916 [==============================] - 0s 91us/sample - loss: 0.1754 - acc: 0.9247 - val_loss: 1.9480 - val_acc: 0.8346 Epoch 295/500 916/916 [==============================] - 0s 80us/sample - loss: 0.1789 - acc: 0.9236 - val_loss: 1.9772 - val_acc: 0.8397 Epoch 296/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1769 - acc: 0.9192 - val_loss: 2.0032 - val_acc: 0.8397 Epoch 297/500 916/916 [==============================] - 0s 75us/sample - loss: 0.1743 - acc: 0.9236 - val_loss: 1.9814 - val_acc: 0.8397 Epoch 298/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1765 - acc: 0.9214 - val_loss: 1.9750 - val_acc: 0.8397 Epoch 299/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1775 - acc: 0.9192 - val_loss: 2.0236 - val_acc: 0.8372 Epoch 300/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1758 - acc: 0.9159 - val_loss: 1.9848 - val_acc: 0.8397 Epoch 301/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1756 - acc: 0.9192 - val_loss: 2.0191 - val_acc: 0.8372 Epoch 302/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1756 - acc: 0.9192 - val_loss: 1.9804 - val_acc: 0.8422 Epoch 303/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1756 - acc: 0.9214 - val_loss: 1.9844 - val_acc: 0.8448 Epoch 304/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1767 - acc: 0.9214 - val_loss: 1.9522 - val_acc: 0.8448 Epoch 305/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1757 - acc: 0.9203 - val_loss: 1.9787 - val_acc: 0.8397 Epoch 306/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1759 - acc: 0.9236 - val_loss: 1.9971 - val_acc: 0.8422 Epoch 307/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1757 - acc: 0.9225 - val_loss: 2.0177 - val_acc: 0.8448 Epoch 308/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1758 - acc: 0.9225 - val_loss: 2.0233 - val_acc: 0.8422 Epoch 309/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1757 - acc: 0.9203 - val_loss: 2.0083 - val_acc: 0.8397 Epoch 310/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1759 - acc: 0.9203 - val_loss: 2.0639 - val_acc: 0.8448 Epoch 311/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1778 - acc: 0.9214 - val_loss: 1.9383 - val_acc: 0.8372 Epoch 312/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1767 - acc: 0.9225 - val_loss: 1.9720 - val_acc: 0.8346 Epoch 313/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1760 - acc: 0.9203 - val_loss: 1.9671 - val_acc: 0.8422 Epoch 314/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1765 - acc: 0.9181 - val_loss: 1.9877 - val_acc: 0.8321 Epoch 315/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1751 - acc: 0.9203 - val_loss: 1.9642 - val_acc: 0.8346 Epoch 316/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1759 - acc: 0.9192 - val_loss: 1.9787 - val_acc: 0.8397 Epoch 317/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1763 - acc: 0.9225 - val_loss: 1.8407 - val_acc: 0.8448 Epoch 318/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1761 - acc: 0.9225 - val_loss: 1.9538 - val_acc: 0.8422 Epoch 319/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1766 - acc: 0.9225 - val_loss: 1.8813 - val_acc: 0.8473 Epoch 320/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1763 - acc: 0.9225 - val_loss: 1.9679 - val_acc: 0.8448 Epoch 321/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1799 - acc: 0.9170 - val_loss: 1.9569 - val_acc: 0.8422 Epoch 322/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1993 - acc: 0.9214 - val_loss: 2.0094 - val_acc: 0.8397 Epoch 323/500 916/916 [==============================] - 0s 69us/sample - loss: 0.2778 - acc: 0.9017 - val_loss: 0.9422 - val_acc: 0.8422 Epoch 324/500 916/916 [==============================] - 0s 61us/sample - loss: 0.2872 - acc: 0.8985 - val_loss: 0.6384 - val_acc: 0.8422 Epoch 325/500 916/916 [==============================] - 0s 54us/sample - loss: 0.2441 - acc: 0.9105 - val_loss: 0.7706 - val_acc: 0.8397 Epoch 326/500 916/916 [==============================] - 0s 54us/sample - loss: 0.2143 - acc: 0.9170 - val_loss: 0.9778 - val_acc: 0.8422 Epoch 327/500 916/916 [==============================] - 0s 54us/sample - loss: 0.2036 - acc: 0.9170 - val_loss: 1.1129 - val_acc: 0.8372 Epoch 328/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1959 - acc: 0.9225 - val_loss: 1.3130 - val_acc: 0.8422 Epoch 329/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1923 - acc: 0.9192 - val_loss: 1.2924 - val_acc: 0.8372 Epoch 330/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1864 - acc: 0.9192 - val_loss: 1.4299 - val_acc: 0.8372 Epoch 331/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1849 - acc: 0.9192 - val_loss: 1.5313 - val_acc: 0.8473 Epoch 332/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1824 - acc: 0.9192 - val_loss: 1.6112 - val_acc: 0.8372 Epoch 333/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1835 - acc: 0.9236 - val_loss: 1.5736 - val_acc: 0.8422 Epoch 334/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1820 - acc: 0.9214 - val_loss: 1.6740 - val_acc: 0.8397 Epoch 335/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1804 - acc: 0.9225 - val_loss: 1.7105 - val_acc: 0.8321 Epoch 336/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1802 - acc: 0.9159 - val_loss: 1.7604 - val_acc: 0.8321 Epoch 337/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1806 - acc: 0.9192 - val_loss: 1.8083 - val_acc: 0.8321 Epoch 338/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1776 - acc: 0.9214 - val_loss: 1.8506 - val_acc: 0.8321 Epoch 339/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1792 - acc: 0.9181 - val_loss: 1.8355 - val_acc: 0.8321 Epoch 340/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1781 - acc: 0.9148 - val_loss: 1.8908 - val_acc: 0.8372 Epoch 341/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1783 - acc: 0.9214 - val_loss: 1.8337 - val_acc: 0.8346 Epoch 342/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1773 - acc: 0.9192 - val_loss: 1.8152 - val_acc: 0.8321 Epoch 343/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1779 - acc: 0.9214 - val_loss: 1.8859 - val_acc: 0.8346 Epoch 344/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1777 - acc: 0.9181 - val_loss: 1.8857 - val_acc: 0.8372 Epoch 345/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1768 - acc: 0.9181 - val_loss: 1.9084 - val_acc: 0.8321 Epoch 346/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1778 - acc: 0.9214 - val_loss: 1.8967 - val_acc: 0.8346 Epoch 347/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1781 - acc: 0.9170 - val_loss: 1.8913 - val_acc: 0.8321 Epoch 348/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1769 - acc: 0.9192 - val_loss: 1.9393 - val_acc: 0.8346 Epoch 349/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1763 - acc: 0.9214 - val_loss: 1.9603 - val_acc: 0.8295 Epoch 350/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1766 - acc: 0.9203 - val_loss: 1.9780 - val_acc: 0.8346 Epoch 351/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1761 - acc: 0.9247 - val_loss: 1.9954 - val_acc: 0.8346 Epoch 352/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1759 - acc: 0.9203 - val_loss: 1.9921 - val_acc: 0.8346 Epoch 353/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1764 - acc: 0.9159 - val_loss: 1.9887 - val_acc: 0.8346 Epoch 354/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1757 - acc: 0.9170 - val_loss: 1.9842 - val_acc: 0.8321 Epoch 355/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1756 - acc: 0.9181 - val_loss: 2.0223 - val_acc: 0.8346 Epoch 356/500
916/916 [==============================] - 0s 55us/sample - loss: 0.1754 - acc: 0.9214 - val_loss: 2.0419 - val_acc: 0.8346 Epoch 357/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1766 - acc: 0.9203 - val_loss: 2.0376 - val_acc: 0.8397 Epoch 358/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1765 - acc: 0.9247 - val_loss: 2.0532 - val_acc: 0.8346 Epoch 359/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1769 - acc: 0.9225 - val_loss: 2.0282 - val_acc: 0.8321 Epoch 360/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1759 - acc: 0.9214 - val_loss: 2.0425 - val_acc: 0.8346 Epoch 361/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1756 - acc: 0.9214 - val_loss: 2.0557 - val_acc: 0.8372 Epoch 362/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1757 - acc: 0.9203 - val_loss: 2.0749 - val_acc: 0.8321 Epoch 363/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1756 - acc: 0.9203 - val_loss: 2.0924 - val_acc: 0.8321 Epoch 364/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1761 - acc: 0.9214 - val_loss: 2.1113 - val_acc: 0.8346 Epoch 365/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1763 - acc: 0.9170 - val_loss: 2.1010 - val_acc: 0.8321 Epoch 366/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1773 - acc: 0.9203 - val_loss: 2.0797 - val_acc: 0.8372 Epoch 367/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1760 - acc: 0.9181 - val_loss: 2.0588 - val_acc: 0.8321 Epoch 368/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1756 - acc: 0.9214 - val_loss: 2.0434 - val_acc: 0.8321 Epoch 369/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1749 - acc: 0.9236 - val_loss: 2.0942 - val_acc: 0.8321 Epoch 370/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1754 - acc: 0.9214 - val_loss: 2.1213 - val_acc: 0.8346 Epoch 371/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1752 - acc: 0.9214 - val_loss: 2.1258 - val_acc: 0.8346 Epoch 372/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1754 - acc: 0.9214 - val_loss: 2.1119 - val_acc: 0.8346 Epoch 373/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1757 - acc: 0.9203 - val_loss: 2.1222 - val_acc: 0.8346 Epoch 374/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1754 - acc: 0.9236 - val_loss: 2.1038 - val_acc: 0.8346 Epoch 375/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1762 - acc: 0.9203 - val_loss: 2.1243 - val_acc: 0.8321 Epoch 376/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1759 - acc: 0.9203 - val_loss: 2.1192 - val_acc: 0.8346 Epoch 377/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1762 - acc: 0.9225 - val_loss: 2.1626 - val_acc: 0.8372 Epoch 378/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1756 - acc: 0.9192 - val_loss: 2.1827 - val_acc: 0.8346 Epoch 379/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1752 - acc: 0.9225 - val_loss: 2.1796 - val_acc: 0.8372 Epoch 380/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1755 - acc: 0.9225 - val_loss: 2.1213 - val_acc: 0.8372 Epoch 381/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1756 - acc: 0.9203 - val_loss: 2.1285 - val_acc: 0.8397 Epoch 382/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1755 - acc: 0.9181 - val_loss: 2.1553 - val_acc: 0.8372 Epoch 383/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1756 - acc: 0.9214 - val_loss: 2.1525 - val_acc: 0.8372 Epoch 384/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1778 - acc: 0.9203 - val_loss: 2.1750 - val_acc: 0.8346 Epoch 385/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1763 - acc: 0.9170 - val_loss: 2.1565 - val_acc: 0.8346 Epoch 386/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1757 - acc: 0.9225 - val_loss: 2.1404 - val_acc: 0.8346 Epoch 387/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1752 - acc: 0.9192 - val_loss: 2.1411 - val_acc: 0.8346 Epoch 388/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1752 - acc: 0.9170 - val_loss: 2.0815 - val_acc: 0.8372 Epoch 389/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1755 - acc: 0.9170 - val_loss: 2.1360 - val_acc: 0.8397 Epoch 390/500 916/916 [==============================] - 0s 71us/sample - loss: 0.1757 - acc: 0.9203 - val_loss: 2.0703 - val_acc: 0.8346 Epoch 391/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1760 - acc: 0.9258 - val_loss: 2.0965 - val_acc: 0.8372 Epoch 392/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1768 - acc: 0.9236 - val_loss: 2.0183 - val_acc: 0.8372 Epoch 393/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1763 - acc: 0.9203 - val_loss: 2.0040 - val_acc: 0.8321 Epoch 394/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1755 - acc: 0.9192 - val_loss: 2.0769 - val_acc: 0.8372 Epoch 395/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1754 - acc: 0.9159 - val_loss: 2.1047 - val_acc: 0.8372 Epoch 396/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1758 - acc: 0.9214 - val_loss: 2.1207 - val_acc: 0.8372 Epoch 397/500 916/916 [==============================] - 0s 51us/sample - loss: 0.1764 - acc: 0.9225 - val_loss: 2.1153 - val_acc: 0.8372 Epoch 398/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1762 - acc: 0.9236 - val_loss: 2.1695 - val_acc: 0.8397 Epoch 399/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1762 - acc: 0.9214 - val_loss: 2.1799 - val_acc: 0.8397 Epoch 400/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1750 - acc: 0.9225 - val_loss: 2.0420 - val_acc: 0.8346 Epoch 401/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1749 - acc: 0.9203 - val_loss: 2.0510 - val_acc: 0.8397 Epoch 402/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1763 - acc: 0.9214 - val_loss: 2.0986 - val_acc: 0.8372 Epoch 403/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1759 - acc: 0.9214 - val_loss: 2.1241 - val_acc: 0.8372 Epoch 404/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1750 - acc: 0.9214 - val_loss: 2.1486 - val_acc: 0.8397 Epoch 405/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1753 - acc: 0.9203 - val_loss: 2.1855 - val_acc: 0.8448 Epoch 406/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1758 - acc: 0.9170 - val_loss: 2.1541 - val_acc: 0.8372 Epoch 407/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1758 - acc: 0.9192 - val_loss: 2.1970 - val_acc: 0.8372 Epoch 408/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1752 - acc: 0.9181 - val_loss: 2.1938 - val_acc: 0.8346 Epoch 409/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1754 - acc: 0.9214 - val_loss: 2.1875 - val_acc: 0.8372 Epoch 410/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1749 - acc: 0.9214 - val_loss: 2.1835 - val_acc: 0.8397 Epoch 411/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1753 - acc: 0.9192 - val_loss: 2.1782 - val_acc: 0.8448 Epoch 412/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1763 - acc: 0.9203 - val_loss: 2.2198 - val_acc: 0.8397 Epoch 413/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1753 - acc: 0.9225 - val_loss: 2.2259 - val_acc: 0.8372 Epoch 414/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1750 - acc: 0.9159 - val_loss: 2.2274 - val_acc: 0.8397 Epoch 415/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1757 - acc: 0.9192 - val_loss: 2.2681 - val_acc: 0.8422 Epoch 416/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1772 - acc: 0.9214 - val_loss: 2.1610 - val_acc: 0.8372 Epoch 417/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1796 - acc: 0.9225 - val_loss: 1.8358 - val_acc: 0.8422 Epoch 418/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1778 - acc: 0.9247 - val_loss: 1.8178 - val_acc: 0.8372 Epoch 419/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1755 - acc: 0.9203 - val_loss: 2.0602 - val_acc: 0.8397 Epoch 420/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1813 - acc: 0.9225 - val_loss: 1.6905 - val_acc: 0.8397 Epoch 421/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1870 - acc: 0.9192 - val_loss: 1.3397 - val_acc: 0.8321 Epoch 422/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1870 - acc: 0.9203 - val_loss: 1.7383 - val_acc: 0.8346 Epoch 423/500 916/916 [==============================] - 0s 56us/sample - loss: 0.2053 - acc: 0.9203 - val_loss: 1.4919 - val_acc: 0.8346 Epoch 424/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1877 - acc: 0.9170 - val_loss: 1.8568 - val_acc: 0.8448 Epoch 425/500 916/916 [==============================] - 0s 55us/sample - loss: 0.2200 - acc: 0.9170 - val_loss: 1.1680 - val_acc: 0.8473 Epoch 426/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1906 - acc: 0.9170 - val_loss: 1.5595 - val_acc: 0.8372 Epoch 427/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1846 - acc: 0.9214 - val_loss: 1.4503 - val_acc: 0.8397 Epoch 428/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1812 - acc: 0.9225 - val_loss: 1.4869 - val_acc: 0.8422 Epoch 429/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1792 - acc: 0.9203 - val_loss: 1.6328 - val_acc: 0.8397 Epoch 430/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1777 - acc: 0.9214 - val_loss: 1.6676 - val_acc: 0.8372 Epoch 431/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1766 - acc: 0.9214 - val_loss: 1.7510 - val_acc: 0.8321 Epoch 432/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1765 - acc: 0.9192 - val_loss: 1.7885 - val_acc: 0.8321 Epoch 433/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1767 - acc: 0.9214 - val_loss: 1.8333 - val_acc: 0.8346 Epoch 434/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1758 - acc: 0.9225 - val_loss: 1.8428 - val_acc: 0.8346 Epoch 435/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1758 - acc: 0.9214 - val_loss: 1.8324 - val_acc: 0.8346 Epoch 436/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1755 - acc: 0.9225 - val_loss: 1.8405 - val_acc: 0.8346 Epoch 437/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1751 - acc: 0.9247 - val_loss: 1.8765 - val_acc: 0.8346 Epoch 438/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1758 - acc: 0.9192 - val_loss: 1.9018 - val_acc: 0.8346 Epoch 439/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1768 - acc: 0.9214 - val_loss: 1.8921 - val_acc: 0.8346 Epoch 440/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1755 - acc: 0.9225 - val_loss: 1.9163 - val_acc: 0.8346 Epoch 441/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1753 - acc: 0.9203 - val_loss: 1.9409 - val_acc: 0.8321 Epoch 442/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1754 - acc: 0.9247 - val_loss: 1.9417 - val_acc: 0.8346 Epoch 443/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1748 - acc: 0.9236 - val_loss: 1.9645 - val_acc: 0.8372 Epoch 444/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1754 - acc: 0.9225 - val_loss: 1.9556 - val_acc: 0.8372 Epoch 445/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1754 - acc: 0.9247 - val_loss: 1.9496 - val_acc: 0.8295 Epoch 446/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1766 - acc: 0.9214 - val_loss: 1.9534 - val_acc: 0.8321 Epoch 447/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1758 - acc: 0.9225 - val_loss: 1.9449 - val_acc: 0.8346 Epoch 448/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1748 - acc: 0.9236 - val_loss: 1.9649 - val_acc: 0.8321 Epoch 449/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1749 - acc: 0.9214 - val_loss: 1.9445 - val_acc: 0.8372 Epoch 450/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1757 - acc: 0.9247 - val_loss: 1.9399 - val_acc: 0.8372 Epoch 451/500 916/916 [==============================] - 0s 52us/sample - loss: 0.1748 - acc: 0.9225 - val_loss: 1.9600 - val_acc: 0.8346 Epoch 452/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1749 - acc: 0.9203 - val_loss: 1.9564 - val_acc: 0.8422 Epoch 453/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1753 - acc: 0.9236 - val_loss: 1.9838 - val_acc: 0.8397 Epoch 454/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1756 - acc: 0.9203 - val_loss: 1.9977 - val_acc: 0.8321 Epoch 455/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1754 - acc: 0.9214 - val_loss: 1.9984 - val_acc: 0.8346 Epoch 456/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1754 - acc: 0.9203 - val_loss: 2.0324 - val_acc: 0.8321 Epoch 457/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1749 - acc: 0.9236 - val_loss: 2.0129 - val_acc: 0.8346 Epoch 458/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1750 - acc: 0.9181 - val_loss: 2.0221 - val_acc: 0.8321 Epoch 459/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1758 - acc: 0.9225 - val_loss: 2.0278 - val_acc: 0.8321 Epoch 460/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1762 - acc: 0.9203 - val_loss: 2.0203 - val_acc: 0.8397 Epoch 461/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1747 - acc: 0.9203 - val_loss: 2.0395 - val_acc: 0.8372 Epoch 462/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1749 - acc: 0.9203 - val_loss: 2.0466 - val_acc: 0.8346 Epoch 463/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1747 - acc: 0.9225 - val_loss: 2.0503 - val_acc: 0.8346 Epoch 464/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1746 - acc: 0.9247 - val_loss: 2.0464 - val_acc: 0.8372 Epoch 465/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1746 - acc: 0.9214 - val_loss: 2.0585 - val_acc: 0.8372 Epoch 466/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1747 - acc: 0.9236 - val_loss: 2.0488 - val_acc: 0.8372 Epoch 467/500 916/916 [==============================] - 0s 55us/sample - loss: 0.1758 - acc: 0.9225 - val_loss: 2.0586 - val_acc: 0.8346 Epoch 468/500 916/916 [==============================] - 0s 69us/sample - loss: 0.1744 - acc: 0.9203 - val_loss: 2.0753 - val_acc: 0.8372 Epoch 469/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1752 - acc: 0.9203 - val_loss: 2.0806 - val_acc: 0.8372 Epoch 470/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1759 - acc: 0.9214 - val_loss: 2.0769 - val_acc: 0.8321 Epoch 471/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1750 - acc: 0.9192 - val_loss: 2.0585 - val_acc: 0.8346 Epoch 472/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1746 - acc: 0.9225 - val_loss: 2.0743 - val_acc: 0.8346 Epoch 473/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1753 - acc: 0.9203 - val_loss: 2.0860 - val_acc: 0.8295 Epoch 474/500
916/916 [==============================] - 0s 62us/sample - loss: 0.1751 - acc: 0.9214 - val_loss: 2.1095 - val_acc: 0.8346 Epoch 475/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1758 - acc: 0.9203 - val_loss: 2.0811 - val_acc: 0.8321 Epoch 476/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1756 - acc: 0.9203 - val_loss: 2.0848 - val_acc: 0.8346 Epoch 477/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1749 - acc: 0.9236 - val_loss: 2.1288 - val_acc: 0.8321 Epoch 478/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1755 - acc: 0.9181 - val_loss: 2.1281 - val_acc: 0.8321 Epoch 479/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1749 - acc: 0.9236 - val_loss: 2.1201 - val_acc: 0.8346 Epoch 480/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1758 - acc: 0.9203 - val_loss: 2.1174 - val_acc: 0.8321 Epoch 481/500 916/916 [==============================] - 0s 53us/sample - loss: 0.1758 - acc: 0.9225 - val_loss: 2.1302 - val_acc: 0.8321 Epoch 482/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1751 - acc: 0.9214 - val_loss: 2.1470 - val_acc: 0.8295 Epoch 483/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1757 - acc: 0.9203 - val_loss: 2.1493 - val_acc: 0.8321 Epoch 484/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1786 - acc: 0.9225 - val_loss: 2.1469 - val_acc: 0.8270 Epoch 485/500 916/916 [==============================] - 0s 70us/sample - loss: 0.1763 - acc: 0.9225 - val_loss: 2.1481 - val_acc: 0.8346 Epoch 486/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1749 - acc: 0.9181 - val_loss: 2.1129 - val_acc: 0.8372 Epoch 487/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1755 - acc: 0.9214 - val_loss: 2.1083 - val_acc: 0.8346 Epoch 488/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1752 - acc: 0.9192 - val_loss: 2.1203 - val_acc: 0.8346 Epoch 489/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1751 - acc: 0.9203 - val_loss: 2.1346 - val_acc: 0.8346 Epoch 490/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1746 - acc: 0.9181 - val_loss: 2.1348 - val_acc: 0.8321 Epoch 491/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1747 - acc: 0.9214 - val_loss: 2.1345 - val_acc: 0.8346 Epoch 492/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1749 - acc: 0.9203 - val_loss: 2.1509 - val_acc: 0.8346 Epoch 493/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1755 - acc: 0.9225 - val_loss: 2.1547 - val_acc: 0.8321 Epoch 494/500 916/916 [==============================] - 0s 54us/sample - loss: 0.1755 - acc: 0.9214 - val_loss: 2.1681 - val_acc: 0.8321 Epoch 495/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1755 - acc: 0.9203 - val_loss: 2.1374 - val_acc: 0.8295 Epoch 496/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1758 - acc: 0.9192 - val_loss: 2.1573 - val_acc: 0.8321 Epoch 497/500 916/916 [==============================] - 0s 77us/sample - loss: 0.1757 - acc: 0.9225 - val_loss: 2.1676 - val_acc: 0.8295 Epoch 498/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1750 - acc: 0.9225 - val_loss: 2.1590 - val_acc: 0.8321 Epoch 499/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1748 - acc: 0.9236 - val_loss: 2.1413 - val_acc: 0.8321 Epoch 500/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1748 - acc: 0.9192 - val_loss: 2.1747 - val_acc: 0.8295 b [[3.15904617e-06 9.99996841e-01] [6.37433350e-01 3.62566650e-01] [3.30711156e-01 6.69288874e-01] ... [4.75158185e-01 5.24841785e-01] [3.73995803e-08 9.99999940e-01] [6.52391016e-02 9.34760928e-01]]
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-225-c36416d297f7> in <module> 26 kernel_width=None,discretize_continuous=True) 27 observation=55 ---> 28 exp = explainer.explain_instance(X_test[observation_1], predict_fn_nn, num_features=5,top_labels=1) NameError: name 'observation_1' is not defined
observation_1=24exp=explainer.explain_instance(X_test[observation_1], predict_fn_nn, num_features=5,top_labels=1)exp.show_in_notebook()| Feature | Value |
| Sex_female | 0.00 |
| name_1 | 0.00 |
| Sex_male | 1.00 |
| Pclass_1 | 0.00 |
| Pclass_3 | 1.00 |
observation_1=42exp=explainer.explain_instance(X_test[observation_1], predict_fn_nn, num_features=5,top_labels=1)exp.show_in_notebook()| Feature | Value |
| name_1 | 0.00 |
| Sex_female | 1.00 |
| Sex_male | 0.00 |
| fare_3 | 0.00 |
| Pclass_3 | 1.00 |
observation_1=98exp=explainer.explain_instance(X_test[observation_1], predict_fn_nn, num_features=5,top_labels=1)exp.show_in_notebook()| Feature | Value |
| Sex_female | 1.00 |
| name_1 | 1.00 |
| Sex_male | 0.00 |
| fare_3 | 0.00 |
| Pclass_3 | 0.00 |
from xgboost import XGBClassifierclasses=['Survived','Not_Survived']all_feat=df_k.columnsmodel_xgb = XGBClassifier()model_xgb.fit(X_train, Y_train)predict_fn_xgb = lambda x: model_xgb.predict_proba(x).astype(float)explainer = lime.lime_tabular.LimeTabularExplainer(X_train,mode='classification',feature_selection= 'auto', class_names=classes,feature_names = all_feat, kernel_width=None,discretize_continuous=True)observation_1=32exp=explainer.explain_instance(X_test[observation_1], predict_fn_xgb, num_features=5,top_labels=1)exp.show_in_notebook()| Feature | Value |
| Sex_female | 1.00 |
| fare_3 | 0.00 |
| Pclass_3 | 1.00 |
| name_2 | 0.00 |
| name_1 | 0.00 |
observation_1=47exp=explainer.explain_instance(X_test[observation_1], predict_fn_xgb, num_features=5,top_labels=1)exp.show_in_notebook()| Feature | Value |
| Sex_female | 0.00 |
| fare_3 | 0.00 |
| name_1 | 0.00 |
| name_2 | 1.00 |
| Pclass_3 | 1.00 |
observation_1=86exp=explainer.explain_instance(X_test[observation_1], predict_fn_xgb, num_features=5,top_labels=1)exp.show_in_notebook()| Feature | Value |
| Sex_female | 0.00 |
| name_2 | 0.00 |
| fare_3 | 0.00 |
| name_1 | 0.00 |
| Pclass_3 | 0.00 |
exp.as_map(){1: [(3, 0.5441379658340547),
(24, -0.15448119146031272),
(2, -0.1233271814431237),
(9, 0.11870380100373268),
(8, -0.11796994360496856)]}exp.as_pyplot_figure()exp.as_list()[('0.00 < Sex_female <= 1.00', 0.5441379658340547),
('fare_3 <= 0.00', -0.15448119146031272),
('0.00 < Pclass_3 <= 1.00', -0.1233271814431237),
('name_2 <= 0.00', 0.11870380100373268),
('name_1 <= 0.00', -0.11796994360496856)]from sklearn.ensemble import RandomForestClassifierclasses=['Survived','Not_Survived']all_feat=df_k.columnsmodel_rf = RandomForestClassifier()model_rf.fit(X_train, Y_train)predict_fn_rf = lambda x: model_rf.predict_proba(x).astype(float)explainer = lime.lime_tabular.LimeTabularExplainer(X_train,mode='classification',feature_selection= 'auto', class_names=classes,feature_names = all_feat, kernel_width=None,discretize_continuous=True)/home/abhijit/.local/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning)
observation_1=47exp=explainer.explain_instance(X_test[observation_1], predict_fn_rf, num_features=5,top_labels=1)exp.show_in_notebook()| Feature | Value |
| name_2 | 1.00 |
| Sex_female | 0.00 |
| Sex_male | 1.00 |
| name_1 | 0.00 |
| Parent_1 | 0.00 |
observation_1=89exp=explainer.explain_instance(X_test[observation_1], predict_fn_rf, num_features=5,top_labels=1)exp.show_in_notebook()| Feature | Value |
| name_2 | 0.00 |
| Sex_female | 1.00 |
| Sex_male | 0.00 |
| name_1 | 0.00 |
| cabin_1 | 1.00 |
exp.as_pyplot_figure()import shapshap_values = shap.TreeExplainer(model_xgb).shap_values(X_train)shap.summary_plot(shap_values, X_train, plot_type="bar")import shapshap_values = shap.TreeExplainer(model_rf).shap_values(X_train)shap.summary_plot(shap_values, X_train, plot_type="bar")model_nn=Neural_Network(X_train,Y_train, X_test,Y_test)Train on 916 samples, validate on 393 samples Epoch 1/500 916/916 [==============================] - 1s 847us/sample - loss: 0.4872 - acc: 0.7795 - val_loss: 0.3605 - val_acc: 0.8550 Epoch 2/500 916/916 [==============================] - 0s 75us/sample - loss: 0.3957 - acc: 0.8603 - val_loss: 0.3708 - val_acc: 0.8626 Epoch 3/500 916/916 [==============================] - 0s 73us/sample - loss: 0.3535 - acc: 0.8635 - val_loss: 0.3465 - val_acc: 0.8651 Epoch 4/500 916/916 [==============================] - 0s 68us/sample - loss: 0.3416 - acc: 0.8668 - val_loss: 0.3367 - val_acc: 0.8677 Epoch 5/500 916/916 [==============================] - 0s 75us/sample - loss: 0.3246 - acc: 0.8766 - val_loss: 0.3454 - val_acc: 0.8677 Epoch 6/500 916/916 [==============================] - 0s 68us/sample - loss: 0.3119 - acc: 0.8854 - val_loss: 0.3537 - val_acc: 0.8626 Epoch 7/500 916/916 [==============================] - 0s 71us/sample - loss: 0.3096 - acc: 0.8832 - val_loss: 0.3455 - val_acc: 0.8702 Epoch 8/500 916/916 [==============================] - 0s 65us/sample - loss: 0.2965 - acc: 0.8865 - val_loss: 0.3491 - val_acc: 0.8702 Epoch 9/500 916/916 [==============================] - 0s 64us/sample - loss: 0.3049 - acc: 0.8821 - val_loss: 0.3641 - val_acc: 0.8728 Epoch 10/500 916/916 [==============================] - 0s 71us/sample - loss: 0.2833 - acc: 0.8919 - val_loss: 0.3634 - val_acc: 0.8550 Epoch 11/500 916/916 [==============================] - 0s 74us/sample - loss: 0.2764 - acc: 0.8974 - val_loss: 0.3891 - val_acc: 0.8728 Epoch 12/500 916/916 [==============================] - 0s 72us/sample - loss: 0.2653 - acc: 0.8963 - val_loss: 0.3988 - val_acc: 0.8473 Epoch 13/500 916/916 [==============================] - 0s 67us/sample - loss: 0.2646 - acc: 0.9017 - val_loss: 0.3856 - val_acc: 0.8499 Epoch 14/500 916/916 [==============================] - 0s 62us/sample - loss: 0.2542 - acc: 0.9116 - val_loss: 0.4260 - val_acc: 0.8601 Epoch 15/500 916/916 [==============================] - 0s 68us/sample - loss: 0.2506 - acc: 0.9017 - val_loss: 0.3894 - val_acc: 0.8626 Epoch 16/500 916/916 [==============================] - 0s 66us/sample - loss: 0.2412 - acc: 0.9061 - val_loss: 0.4332 - val_acc: 0.8499 Epoch 17/500 916/916 [==============================] - 0s 63us/sample - loss: 0.2377 - acc: 0.9061 - val_loss: 0.4575 - val_acc: 0.8448 Epoch 18/500 916/916 [==============================] - 0s 72us/sample - loss: 0.2321 - acc: 0.9138 - val_loss: 0.4667 - val_acc: 0.8499 Epoch 19/500 916/916 [==============================] - 0s 63us/sample - loss: 0.2296 - acc: 0.9116 - val_loss: 0.4489 - val_acc: 0.8473 Epoch 20/500 916/916 [==============================] - 0s 62us/sample - loss: 0.2292 - acc: 0.9083 - val_loss: 0.4783 - val_acc: 0.8550 Epoch 21/500 916/916 [==============================] - 0s 64us/sample - loss: 0.2231 - acc: 0.9105 - val_loss: 0.4535 - val_acc: 0.8397 Epoch 22/500 916/916 [==============================] - 0s 72us/sample - loss: 0.2314 - acc: 0.9105 - val_loss: 0.4535 - val_acc: 0.8550 Epoch 23/500 916/916 [==============================] - 0s 70us/sample - loss: 0.2167 - acc: 0.9148 - val_loss: 0.4906 - val_acc: 0.8601 Epoch 24/500 916/916 [==============================] - 0s 70us/sample - loss: 0.2165 - acc: 0.9148 - val_loss: 0.5205 - val_acc: 0.8575 Epoch 25/500 916/916 [==============================] - 0s 72us/sample - loss: 0.2165 - acc: 0.9148 - val_loss: 0.4611 - val_acc: 0.8524 Epoch 26/500 916/916 [==============================] - 0s 61us/sample - loss: 0.2099 - acc: 0.9192 - val_loss: 0.5398 - val_acc: 0.8499 Epoch 27/500 916/916 [==============================] - 0s 63us/sample - loss: 0.2120 - acc: 0.9170 - val_loss: 0.5406 - val_acc: 0.8524 Epoch 28/500 916/916 [==============================] - 0s 68us/sample - loss: 0.2085 - acc: 0.9148 - val_loss: 0.5516 - val_acc: 0.8550 Epoch 29/500 916/916 [==============================] - 0s 65us/sample - loss: 0.2035 - acc: 0.9170 - val_loss: 0.5634 - val_acc: 0.8550 Epoch 30/500 916/916 [==============================] - 0s 61us/sample - loss: 0.2006 - acc: 0.9181 - val_loss: 0.5571 - val_acc: 0.8601 Epoch 31/500 916/916 [==============================] - 0s 68us/sample - loss: 0.2040 - acc: 0.9159 - val_loss: 0.5320 - val_acc: 0.8524 Epoch 32/500 916/916 [==============================] - 0s 65us/sample - loss: 0.2034 - acc: 0.9170 - val_loss: 0.5614 - val_acc: 0.8575 Epoch 33/500 916/916 [==============================] - 0s 61us/sample - loss: 0.2030 - acc: 0.9203 - val_loss: 0.5702 - val_acc: 0.8575 Epoch 34/500 916/916 [==============================] - 0s 68us/sample - loss: 0.2031 - acc: 0.9116 - val_loss: 0.5850 - val_acc: 0.8575 Epoch 35/500 916/916 [==============================] - 0s 74us/sample - loss: 0.2048 - acc: 0.9203 - val_loss: 0.5634 - val_acc: 0.8473 Epoch 36/500 916/916 [==============================] - 0s 74us/sample - loss: 0.1953 - acc: 0.9203 - val_loss: 0.6360 - val_acc: 0.8499 Epoch 37/500 916/916 [==============================] - 0s 70us/sample - loss: 0.1984 - acc: 0.9138 - val_loss: 0.6103 - val_acc: 0.8524 Epoch 38/500 916/916 [==============================] - 0s 70us/sample - loss: 0.1948 - acc: 0.9181 - val_loss: 0.6227 - val_acc: 0.8550 Epoch 39/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1923 - acc: 0.9203 - val_loss: 0.6578 - val_acc: 0.8499 Epoch 40/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1938 - acc: 0.9236 - val_loss: 0.6597 - val_acc: 0.8524 Epoch 41/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1905 - acc: 0.9203 - val_loss: 0.6705 - val_acc: 0.8499 Epoch 42/500 916/916 [==============================] - 0s 70us/sample - loss: 0.1925 - acc: 0.9170 - val_loss: 0.6753 - val_acc: 0.8499 Epoch 43/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1909 - acc: 0.9203 - val_loss: 0.7018 - val_acc: 0.8524 Epoch 44/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1974 - acc: 0.9159 - val_loss: 0.6476 - val_acc: 0.8524 Epoch 45/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1905 - acc: 0.9225 - val_loss: 0.7397 - val_acc: 0.8448 Epoch 46/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1911 - acc: 0.9148 - val_loss: 0.7274 - val_acc: 0.8448 Epoch 47/500 916/916 [==============================] - 0s 72us/sample - loss: 0.1881 - acc: 0.9170 - val_loss: 0.7389 - val_acc: 0.8499 Epoch 48/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1925 - acc: 0.9203 - val_loss: 0.6945 - val_acc: 0.8499 Epoch 49/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1905 - acc: 0.9203 - val_loss: 0.7610 - val_acc: 0.8473 Epoch 50/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1882 - acc: 0.9203 - val_loss: 0.7720 - val_acc: 0.8473 Epoch 51/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1874 - acc: 0.9170 - val_loss: 0.7977 - val_acc: 0.8473 Epoch 52/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1905 - acc: 0.9181 - val_loss: 0.7236 - val_acc: 0.8422 Epoch 53/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1905 - acc: 0.9170 - val_loss: 0.7418 - val_acc: 0.8473 Epoch 54/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1883 - acc: 0.9192 - val_loss: 0.7669 - val_acc: 0.8422 Epoch 55/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1859 - acc: 0.9181 - val_loss: 0.7823 - val_acc: 0.8473 Epoch 56/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1847 - acc: 0.9192 - val_loss: 0.8230 - val_acc: 0.8448 Epoch 57/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1853 - acc: 0.9225 - val_loss: 0.8005 - val_acc: 0.8473 Epoch 58/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1870 - acc: 0.9192 - val_loss: 0.8011 - val_acc: 0.8448 Epoch 59/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1862 - acc: 0.9192 - val_loss: 0.8166 - val_acc: 0.8448 Epoch 60/500 916/916 [==============================] - 0s 71us/sample - loss: 0.1858 - acc: 0.9203 - val_loss: 0.8178 - val_acc: 0.8524 Epoch 61/500 916/916 [==============================] - 0s 77us/sample - loss: 0.1846 - acc: 0.9203 - val_loss: 0.8994 - val_acc: 0.8422 Epoch 62/500 916/916 [==============================] - 0s 79us/sample - loss: 0.1838 - acc: 0.9214 - val_loss: 0.8333 - val_acc: 0.8499 Epoch 63/500 916/916 [==============================] - 0s 81us/sample - loss: 0.1849 - acc: 0.9181 - val_loss: 0.8580 - val_acc: 0.8473 Epoch 64/500 916/916 [==============================] - 0s 69us/sample - loss: 0.1855 - acc: 0.9170 - val_loss: 0.9176 - val_acc: 0.8448 Epoch 65/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1837 - acc: 0.9225 - val_loss: 0.8286 - val_acc: 0.8499 Epoch 66/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1850 - acc: 0.9192 - val_loss: 0.9298 - val_acc: 0.8448 Epoch 67/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1842 - acc: 0.9225 - val_loss: 0.8229 - val_acc: 0.8422 Epoch 68/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1839 - acc: 0.9225 - val_loss: 0.9907 - val_acc: 0.8397 Epoch 69/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1878 - acc: 0.9214 - val_loss: 0.9295 - val_acc: 0.8473 Epoch 70/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1921 - acc: 0.9203 - val_loss: 0.8945 - val_acc: 0.8499 Epoch 71/500 916/916 [==============================] - 0s 59us/sample - loss: 0.2111 - acc: 0.9170 - val_loss: 0.7426 - val_acc: 0.8397 Epoch 72/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1926 - acc: 0.9181 - val_loss: 0.8224 - val_acc: 0.8601 Epoch 73/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1932 - acc: 0.9192 - val_loss: 0.8506 - val_acc: 0.8473 Epoch 74/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1853 - acc: 0.9203 - val_loss: 0.9098 - val_acc: 0.8448 Epoch 75/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1863 - acc: 0.9181 - val_loss: 0.9283 - val_acc: 0.8473 Epoch 76/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1816 - acc: 0.9192 - val_loss: 0.9440 - val_acc: 0.8499 Epoch 77/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1831 - acc: 0.9225 - val_loss: 0.9670 - val_acc: 0.8575 Epoch 78/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1834 - acc: 0.9203 - val_loss: 0.9560 - val_acc: 0.8499 Epoch 79/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1830 - acc: 0.9192 - val_loss: 0.9634 - val_acc: 0.8422 Epoch 80/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1814 - acc: 0.9214 - val_loss: 0.9999 - val_acc: 0.8499 Epoch 81/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1793 - acc: 0.9225 - val_loss: 0.9654 - val_acc: 0.8397 Epoch 82/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1818 - acc: 0.9148 - val_loss: 0.9721 - val_acc: 0.8448 Epoch 83/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1792 - acc: 0.9225 - val_loss: 1.0093 - val_acc: 0.8473 Epoch 84/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1799 - acc: 0.9214 - val_loss: 0.9973 - val_acc: 0.8473 Epoch 85/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1796 - acc: 0.9214 - val_loss: 1.0079 - val_acc: 0.8448 Epoch 86/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1788 - acc: 0.9203 - val_loss: 1.0678 - val_acc: 0.8473 Epoch 87/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1802 - acc: 0.9170 - val_loss: 1.0129 - val_acc: 0.8448 Epoch 88/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1790 - acc: 0.9214 - val_loss: 1.0234 - val_acc: 0.8473 Epoch 89/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1786 - acc: 0.9214 - val_loss: 1.0800 - val_acc: 0.8448 Epoch 90/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1830 - acc: 0.9159 - val_loss: 1.0400 - val_acc: 0.8448 Epoch 91/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1790 - acc: 0.9225 - val_loss: 1.0374 - val_acc: 0.8473 Epoch 92/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1818 - acc: 0.9225 - val_loss: 1.0380 - val_acc: 0.8499 Epoch 93/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1826 - acc: 0.9214 - val_loss: 1.0500 - val_acc: 0.8473 Epoch 94/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1796 - acc: 0.9214 - val_loss: 1.0500 - val_acc: 0.8448 Epoch 95/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1802 - acc: 0.9247 - val_loss: 1.0617 - val_acc: 0.8473 Epoch 96/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1800 - acc: 0.9269 - val_loss: 1.0342 - val_acc: 0.8473 Epoch 97/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1819 - acc: 0.9159 - val_loss: 1.0605 - val_acc: 0.8448 Epoch 98/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1783 - acc: 0.9181 - val_loss: 1.1040 - val_acc: 0.8499 Epoch 99/500 916/916 [==============================] - 0s 69us/sample - loss: 0.1783 - acc: 0.9203 - val_loss: 1.0879 - val_acc: 0.8473 Epoch 100/500 916/916 [==============================] - 0s 92us/sample - loss: 0.1794 - acc: 0.9214 - val_loss: 1.0399 - val_acc: 0.8499 Epoch 101/500 916/916 [==============================] - 0s 98us/sample - loss: 0.1793 - acc: 0.9236 - val_loss: 1.1032 - val_acc: 0.8448 Epoch 102/500 916/916 [==============================] - 0s 79us/sample - loss: 0.1797 - acc: 0.9225 - val_loss: 1.0484 - val_acc: 0.8422 Epoch 103/500 916/916 [==============================] - 0s 75us/sample - loss: 0.1795 - acc: 0.9192 - val_loss: 1.0792 - val_acc: 0.8473 Epoch 104/500 916/916 [==============================] - 0s 78us/sample - loss: 0.1798 - acc: 0.9247 - val_loss: 1.1376 - val_acc: 0.8473 Epoch 105/500 916/916 [==============================] - 0s 79us/sample - loss: 0.1788 - acc: 0.9170 - val_loss: 1.1153 - val_acc: 0.8473 Epoch 106/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1784 - acc: 0.9203 - val_loss: 1.1619 - val_acc: 0.8473 Epoch 107/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1790 - acc: 0.9181 - val_loss: 1.1312 - val_acc: 0.8473 Epoch 108/500 916/916 [==============================] - ETA: 0s - loss: 0.0735 - acc: 1.000 - 0s 61us/sample - loss: 0.1800 - acc: 0.9236 - val_loss: 1.1222 - val_acc: 0.8499 Epoch 109/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1792 - acc: 0.9236 - val_loss: 1.1277 - val_acc: 0.8448 Epoch 110/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1786 - acc: 0.9148 - val_loss: 1.1511 - val_acc: 0.8422 Epoch 111/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1782 - acc: 0.9214 - val_loss: 1.1407 - val_acc: 0.8473 Epoch 112/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1815 - acc: 0.9203 - val_loss: 1.1392 - val_acc: 0.8422 Epoch 113/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1841 - acc: 0.9269 - val_loss: 1.0748 - val_acc: 0.8499 Epoch 114/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1801 - acc: 0.9214 - val_loss: 1.0972 - val_acc: 0.8448 Epoch 115/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1809 - acc: 0.9225 - val_loss: 1.0711 - val_acc: 0.8448 Epoch 116/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1788 - acc: 0.9225 - val_loss: 1.1497 - val_acc: 0.8473 Epoch 117/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1782 - acc: 0.9225 - val_loss: 1.2075 - val_acc: 0.8422 Epoch 118/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1794 - acc: 0.9203 - val_loss: 1.1924 - val_acc: 0.8448 Epoch 119/500
916/916 [==============================] - 0s 58us/sample - loss: 0.1788 - acc: 0.9192 - val_loss: 1.1529 - val_acc: 0.8422 Epoch 120/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1791 - acc: 0.9214 - val_loss: 1.1898 - val_acc: 0.8499 Epoch 121/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1773 - acc: 0.9203 - val_loss: 1.1975 - val_acc: 0.8422 Epoch 122/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1793 - acc: 0.9214 - val_loss: 1.2165 - val_acc: 0.8499 Epoch 123/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1784 - acc: 0.9214 - val_loss: 1.1711 - val_acc: 0.8448 Epoch 124/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1774 - acc: 0.9181 - val_loss: 1.1901 - val_acc: 0.8422 Epoch 125/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1771 - acc: 0.9203 - val_loss: 1.2177 - val_acc: 0.8422 Epoch 126/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1774 - acc: 0.9225 - val_loss: 1.1961 - val_acc: 0.8422 Epoch 127/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1769 - acc: 0.9214 - val_loss: 1.2117 - val_acc: 0.8448 Epoch 128/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1774 - acc: 0.9236 - val_loss: 1.1994 - val_acc: 0.8448 Epoch 129/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1778 - acc: 0.9181 - val_loss: 1.2142 - val_acc: 0.8473 Epoch 130/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1788 - acc: 0.9214 - val_loss: 1.1838 - val_acc: 0.8422 Epoch 131/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1793 - acc: 0.9236 - val_loss: 1.1592 - val_acc: 0.8473 Epoch 132/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1770 - acc: 0.9203 - val_loss: 1.1975 - val_acc: 0.8448 Epoch 133/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1802 - acc: 0.9214 - val_loss: 1.2048 - val_acc: 0.8448 Epoch 134/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1803 - acc: 0.9214 - val_loss: 1.2311 - val_acc: 0.8422 Epoch 135/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1773 - acc: 0.9236 - val_loss: 1.2238 - val_acc: 0.8422 Epoch 136/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1773 - acc: 0.9247 - val_loss: 1.2279 - val_acc: 0.8448 Epoch 137/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1780 - acc: 0.9203 - val_loss: 1.2356 - val_acc: 0.8448 Epoch 138/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1789 - acc: 0.9247 - val_loss: 1.2141 - val_acc: 0.8422 Epoch 139/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1779 - acc: 0.9247 - val_loss: 1.2344 - val_acc: 0.8473 Epoch 140/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1769 - acc: 0.9192 - val_loss: 1.2327 - val_acc: 0.8448 Epoch 141/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1781 - acc: 0.9203 - val_loss: 1.2281 - val_acc: 0.8422 Epoch 142/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1800 - acc: 0.9225 - val_loss: 1.2035 - val_acc: 0.8473 Epoch 143/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1799 - acc: 0.9192 - val_loss: 1.2349 - val_acc: 0.8448 Epoch 144/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1815 - acc: 0.9236 - val_loss: 1.2808 - val_acc: 0.8473 Epoch 145/500 916/916 [==============================] - 0s 77us/sample - loss: 0.1813 - acc: 0.9192 - val_loss: 1.1743 - val_acc: 0.8448 Epoch 146/500 916/916 [==============================] - 0s 80us/sample - loss: 0.1782 - acc: 0.9225 - val_loss: 1.2433 - val_acc: 0.8448 Epoch 147/500 916/916 [==============================] - 0s 77us/sample - loss: 0.1783 - acc: 0.9203 - val_loss: 1.2728 - val_acc: 0.8473 Epoch 148/500 916/916 [==============================] - 0s 79us/sample - loss: 0.1803 - acc: 0.9192 - val_loss: 1.2528 - val_acc: 0.8422 Epoch 149/500 916/916 [==============================] - 0s 79us/sample - loss: 0.1768 - acc: 0.9203 - val_loss: 1.2662 - val_acc: 0.8473 Epoch 150/500 916/916 [==============================] - 0s 71us/sample - loss: 0.1797 - acc: 0.9214 - val_loss: 1.3102 - val_acc: 0.8422 Epoch 151/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1804 - acc: 0.9214 - val_loss: 1.2898 - val_acc: 0.8499 Epoch 152/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1808 - acc: 0.9203 - val_loss: 1.2239 - val_acc: 0.8422 Epoch 153/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1864 - acc: 0.9214 - val_loss: 1.3434 - val_acc: 0.8448 Epoch 154/500 916/916 [==============================] - 0s 89us/sample - loss: 0.2482 - acc: 0.9116 - val_loss: 0.9935 - val_acc: 0.8346 Epoch 155/500 916/916 [==============================] - 0s 74us/sample - loss: 0.2868 - acc: 0.8908 - val_loss: 0.5153 - val_acc: 0.8473 Epoch 156/500 916/916 [==============================] - 0s 66us/sample - loss: 0.2473 - acc: 0.9072 - val_loss: 0.6170 - val_acc: 0.8397 Epoch 157/500 916/916 [==============================] - 0s 71us/sample - loss: 0.2268 - acc: 0.9083 - val_loss: 0.5353 - val_acc: 0.8397 Epoch 158/500 916/916 [==============================] - 0s 64us/sample - loss: 0.2072 - acc: 0.9225 - val_loss: 0.8992 - val_acc: 0.8499 Epoch 159/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1965 - acc: 0.9203 - val_loss: 0.8392 - val_acc: 0.8473 Epoch 160/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1917 - acc: 0.9159 - val_loss: 0.9701 - val_acc: 0.8422 Epoch 161/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1866 - acc: 0.9236 - val_loss: 1.0626 - val_acc: 0.8499 Epoch 162/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1846 - acc: 0.9192 - val_loss: 1.1126 - val_acc: 0.8422 Epoch 163/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1855 - acc: 0.9214 - val_loss: 1.1208 - val_acc: 0.8473 Epoch 164/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1851 - acc: 0.9214 - val_loss: 1.1352 - val_acc: 0.8448 Epoch 165/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1820 - acc: 0.9225 - val_loss: 1.1759 - val_acc: 0.8473 Epoch 166/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1782 - acc: 0.9247 - val_loss: 1.2498 - val_acc: 0.8473 Epoch 167/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1800 - acc: 0.9203 - val_loss: 1.2033 - val_acc: 0.8473 Epoch 168/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1798 - acc: 0.9192 - val_loss: 1.2123 - val_acc: 0.8422 Epoch 169/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1794 - acc: 0.9203 - val_loss: 1.2657 - val_acc: 0.8422 Epoch 170/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1777 - acc: 0.9192 - val_loss: 1.2999 - val_acc: 0.8422 Epoch 171/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1787 - acc: 0.9203 - val_loss: 1.2736 - val_acc: 0.8422 Epoch 172/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1807 - acc: 0.9214 - val_loss: 1.2819 - val_acc: 0.8448 Epoch 173/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1788 - acc: 0.9225 - val_loss: 1.2926 - val_acc: 0.8473 Epoch 174/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1779 - acc: 0.9236 - val_loss: 1.3076 - val_acc: 0.8422 Epoch 175/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1774 - acc: 0.9214 - val_loss: 1.3223 - val_acc: 0.8397 Epoch 176/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1786 - acc: 0.9214 - val_loss: 1.3125 - val_acc: 0.8397 Epoch 177/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1793 - acc: 0.9225 - val_loss: 1.3007 - val_acc: 0.8422 Epoch 178/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1775 - acc: 0.9214 - val_loss: 1.3146 - val_acc: 0.8422 Epoch 179/500 916/916 [==============================] - 0s 69us/sample - loss: 0.1766 - acc: 0.9225 - val_loss: 1.3088 - val_acc: 0.8422 Epoch 180/500 916/916 [==============================] - 0s 98us/sample - loss: 0.1776 - acc: 0.9203 - val_loss: 1.3268 - val_acc: 0.8448 Epoch 181/500 916/916 [==============================] - 0s 82us/sample - loss: 0.1787 - acc: 0.9203 - val_loss: 1.3629 - val_acc: 0.8422 Epoch 182/500 916/916 [==============================] - 0s 72us/sample - loss: 0.1782 - acc: 0.9192 - val_loss: 1.3544 - val_acc: 0.8397 Epoch 183/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1774 - acc: 0.9181 - val_loss: 1.3679 - val_acc: 0.8422 Epoch 184/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1775 - acc: 0.9203 - val_loss: 1.3758 - val_acc: 0.8422 Epoch 185/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1771 - acc: 0.9203 - val_loss: 1.3916 - val_acc: 0.8422 Epoch 186/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1797 - acc: 0.9192 - val_loss: 1.3725 - val_acc: 0.8422 Epoch 187/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1780 - acc: 0.9214 - val_loss: 1.2688 - val_acc: 0.8448 Epoch 188/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1775 - acc: 0.9214 - val_loss: 1.3487 - val_acc: 0.8473 Epoch 189/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1772 - acc: 0.9192 - val_loss: 1.3611 - val_acc: 0.8499 Epoch 190/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1765 - acc: 0.9214 - val_loss: 1.3641 - val_acc: 0.8473 Epoch 191/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1770 - acc: 0.9192 - val_loss: 1.3717 - val_acc: 0.8448 Epoch 192/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1774 - acc: 0.9225 - val_loss: 1.3837 - val_acc: 0.8422 Epoch 193/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1770 - acc: 0.9203 - val_loss: 1.3687 - val_acc: 0.8448 Epoch 194/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1770 - acc: 0.9236 - val_loss: 1.3992 - val_acc: 0.8397 Epoch 195/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1777 - acc: 0.9181 - val_loss: 1.4101 - val_acc: 0.8422 Epoch 196/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1771 - acc: 0.9192 - val_loss: 1.4119 - val_acc: 0.8422 Epoch 197/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1790 - acc: 0.9225 - val_loss: 1.3896 - val_acc: 0.8422 Epoch 198/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1768 - acc: 0.9192 - val_loss: 1.4252 - val_acc: 0.8422 Epoch 199/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1765 - acc: 0.9181 - val_loss: 1.4255 - val_acc: 0.8422 Epoch 200/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1759 - acc: 0.9170 - val_loss: 1.4128 - val_acc: 0.8422 Epoch 201/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1769 - acc: 0.9236 - val_loss: 1.4493 - val_acc: 0.8397 Epoch 202/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1768 - acc: 0.9236 - val_loss: 1.4341 - val_acc: 0.8397 Epoch 203/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1765 - acc: 0.9159 - val_loss: 1.4191 - val_acc: 0.8448 Epoch 204/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1763 - acc: 0.9181 - val_loss: 1.4301 - val_acc: 0.8397 Epoch 205/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1783 - acc: 0.9214 - val_loss: 1.4297 - val_acc: 0.8524 Epoch 206/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1766 - acc: 0.9192 - val_loss: 1.4411 - val_acc: 0.8397 Epoch 207/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1768 - acc: 0.9214 - val_loss: 1.4424 - val_acc: 0.8397 Epoch 208/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1767 - acc: 0.9170 - val_loss: 1.4302 - val_acc: 0.8397 Epoch 209/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1771 - acc: 0.9258 - val_loss: 1.4304 - val_acc: 0.8448 Epoch 210/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1782 - acc: 0.9214 - val_loss: 1.4208 - val_acc: 0.8473 Epoch 211/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1777 - acc: 0.9192 - val_loss: 1.4181 - val_acc: 0.8422 Epoch 212/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1764 - acc: 0.9181 - val_loss: 1.4037 - val_acc: 0.8422 Epoch 213/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1767 - acc: 0.9203 - val_loss: 1.4400 - val_acc: 0.8473 Epoch 214/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1765 - acc: 0.9203 - val_loss: 1.4632 - val_acc: 0.8372 Epoch 215/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1760 - acc: 0.9170 - val_loss: 1.4286 - val_acc: 0.8448 Epoch 216/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1769 - acc: 0.9203 - val_loss: 1.4498 - val_acc: 0.8397 Epoch 217/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1766 - acc: 0.9192 - val_loss: 1.4697 - val_acc: 0.8422 Epoch 218/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1774 - acc: 0.9203 - val_loss: 1.4578 - val_acc: 0.8499 Epoch 219/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1773 - acc: 0.9214 - val_loss: 1.4470 - val_acc: 0.8499 Epoch 220/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1763 - acc: 0.9203 - val_loss: 1.4533 - val_acc: 0.8448 Epoch 221/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1768 - acc: 0.9203 - val_loss: 1.4709 - val_acc: 0.8397 Epoch 222/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1775 - acc: 0.9203 - val_loss: 1.3780 - val_acc: 0.8473 Epoch 223/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1776 - acc: 0.9225 - val_loss: 1.4366 - val_acc: 0.8422 Epoch 224/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1775 - acc: 0.9225 - val_loss: 1.4492 - val_acc: 0.8473 Epoch 225/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1803 - acc: 0.9181 - val_loss: 1.4058 - val_acc: 0.8422 Epoch 226/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1788 - acc: 0.9225 - val_loss: 1.4173 - val_acc: 0.8499 Epoch 227/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1770 - acc: 0.9192 - val_loss: 1.4387 - val_acc: 0.8397 Epoch 228/500 916/916 [==============================] - 0s 72us/sample - loss: 0.1771 - acc: 0.9247 - val_loss: 1.4644 - val_acc: 0.8422 Epoch 229/500 916/916 [==============================] - 0s 80us/sample - loss: 0.1770 - acc: 0.9214 - val_loss: 1.4574 - val_acc: 0.8397 Epoch 230/500 916/916 [==============================] - 0s 77us/sample - loss: 0.1761 - acc: 0.9203 - val_loss: 1.4742 - val_acc: 0.8397 Epoch 231/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1770 - acc: 0.9225 - val_loss: 1.4774 - val_acc: 0.8372 Epoch 232/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1769 - acc: 0.9247 - val_loss: 1.4866 - val_acc: 0.8422 Epoch 233/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1770 - acc: 0.9203 - val_loss: 1.4879 - val_acc: 0.8397 Epoch 234/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1764 - acc: 0.9214 - val_loss: 1.4510 - val_acc: 0.8422 Epoch 235/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1764 - acc: 0.9214 - val_loss: 1.4461 - val_acc: 0.8422 Epoch 236/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1767 - acc: 0.9203 - val_loss: 1.4651 - val_acc: 0.8448 Epoch 237/500
916/916 [==============================] - 0s 63us/sample - loss: 0.1771 - acc: 0.9181 - val_loss: 1.4932 - val_acc: 0.8473 Epoch 238/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1764 - acc: 0.9214 - val_loss: 1.4591 - val_acc: 0.8372 Epoch 239/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1757 - acc: 0.9247 - val_loss: 1.4982 - val_acc: 0.8448 Epoch 240/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1771 - acc: 0.9225 - val_loss: 1.4977 - val_acc: 0.8448 Epoch 241/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1768 - acc: 0.9192 - val_loss: 1.4648 - val_acc: 0.8397 Epoch 242/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1771 - acc: 0.9159 - val_loss: 1.4568 - val_acc: 0.8397 Epoch 243/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1760 - acc: 0.9214 - val_loss: 1.4650 - val_acc: 0.8372 Epoch 244/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1769 - acc: 0.9214 - val_loss: 1.4914 - val_acc: 0.8422 Epoch 245/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1762 - acc: 0.9214 - val_loss: 1.4995 - val_acc: 0.8372 Epoch 246/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1772 - acc: 0.9236 - val_loss: 1.5105 - val_acc: 0.8397 Epoch 247/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1769 - acc: 0.9181 - val_loss: 1.5046 - val_acc: 0.8372 Epoch 248/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1771 - acc: 0.9170 - val_loss: 1.5144 - val_acc: 0.8397 Epoch 249/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1768 - acc: 0.9214 - val_loss: 1.5001 - val_acc: 0.8372 Epoch 250/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1759 - acc: 0.9181 - val_loss: 1.4953 - val_acc: 0.8397 Epoch 251/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1771 - acc: 0.9225 - val_loss: 1.5268 - val_acc: 0.8346 Epoch 252/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1777 - acc: 0.9214 - val_loss: 1.4643 - val_acc: 0.8422 Epoch 253/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1775 - acc: 0.9236 - val_loss: 1.4717 - val_acc: 0.8397 Epoch 254/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1766 - acc: 0.9181 - val_loss: 1.5461 - val_acc: 0.8397 Epoch 255/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1772 - acc: 0.9214 - val_loss: 1.4220 - val_acc: 0.8473 Epoch 256/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1784 - acc: 0.9214 - val_loss: 1.4244 - val_acc: 0.8473 Epoch 257/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1821 - acc: 0.9214 - val_loss: 1.2176 - val_acc: 0.8295 Epoch 258/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1875 - acc: 0.9181 - val_loss: 1.3607 - val_acc: 0.8422 Epoch 259/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1810 - acc: 0.9203 - val_loss: 1.4538 - val_acc: 0.8321 Epoch 260/500 916/916 [==============================] - 0s 60us/sample - loss: 0.2099 - acc: 0.9170 - val_loss: 1.2512 - val_acc: 0.8448 Epoch 261/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1960 - acc: 0.9159 - val_loss: 1.1005 - val_acc: 0.8499 Epoch 262/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1944 - acc: 0.9192 - val_loss: 1.2581 - val_acc: 0.8295 Epoch 263/500 916/916 [==============================] - 0s 60us/sample - loss: 0.2192 - acc: 0.9105 - val_loss: 1.1369 - val_acc: 0.8422 Epoch 264/500 916/916 [==============================] - 0s 63us/sample - loss: 0.2508 - acc: 0.9050 - val_loss: 0.7071 - val_acc: 0.8448 Epoch 265/500 916/916 [==============================] - 0s 61us/sample - loss: 0.2086 - acc: 0.9181 - val_loss: 0.8403 - val_acc: 0.8422 Epoch 266/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1945 - acc: 0.9203 - val_loss: 1.1940 - val_acc: 0.8448 Epoch 267/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1908 - acc: 0.9258 - val_loss: 1.0851 - val_acc: 0.8346 Epoch 268/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1827 - acc: 0.9225 - val_loss: 1.1971 - val_acc: 0.8448 Epoch 269/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1796 - acc: 0.9225 - val_loss: 1.2798 - val_acc: 0.8448 Epoch 270/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1774 - acc: 0.9214 - val_loss: 1.2839 - val_acc: 0.8448 Epoch 271/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1780 - acc: 0.9225 - val_loss: 1.3463 - val_acc: 0.8448 Epoch 272/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1776 - acc: 0.9214 - val_loss: 1.3662 - val_acc: 0.8448 Epoch 273/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1768 - acc: 0.9225 - val_loss: 1.3849 - val_acc: 0.8473 Epoch 274/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1765 - acc: 0.9225 - val_loss: 1.3974 - val_acc: 0.8473 Epoch 275/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1764 - acc: 0.9225 - val_loss: 1.4134 - val_acc: 0.8473 Epoch 276/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1776 - acc: 0.9225 - val_loss: 1.3785 - val_acc: 0.8473 Epoch 277/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1768 - acc: 0.9214 - val_loss: 1.4143 - val_acc: 0.8448 Epoch 278/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1757 - acc: 0.9203 - val_loss: 1.4382 - val_acc: 0.8473 Epoch 279/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1761 - acc: 0.9214 - val_loss: 1.4371 - val_acc: 0.8499 Epoch 280/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1769 - acc: 0.9225 - val_loss: 1.4487 - val_acc: 0.8448 Epoch 281/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1758 - acc: 0.9214 - val_loss: 1.4327 - val_acc: 0.8473 Epoch 282/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1774 - acc: 0.9225 - val_loss: 1.4436 - val_acc: 0.8448 Epoch 283/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1772 - acc: 0.9203 - val_loss: 1.4728 - val_acc: 0.8448 Epoch 284/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1766 - acc: 0.9203 - val_loss: 1.4579 - val_acc: 0.8448 Epoch 285/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1779 - acc: 0.9203 - val_loss: 1.4516 - val_acc: 0.8473 Epoch 286/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1761 - acc: 0.9225 - val_loss: 1.4758 - val_acc: 0.8448 Epoch 287/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1758 - acc: 0.9225 - val_loss: 1.4779 - val_acc: 0.8448 Epoch 288/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1763 - acc: 0.9214 - val_loss: 1.4982 - val_acc: 0.8448 Epoch 289/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1774 - acc: 0.9192 - val_loss: 1.4875 - val_acc: 0.8448 Epoch 290/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1763 - acc: 0.9236 - val_loss: 1.5012 - val_acc: 0.8422 Epoch 291/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1760 - acc: 0.9203 - val_loss: 1.4921 - val_acc: 0.8473 Epoch 292/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1749 - acc: 0.9192 - val_loss: 1.4770 - val_acc: 0.8448 Epoch 293/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1763 - acc: 0.9170 - val_loss: 1.4845 - val_acc: 0.8473 Epoch 294/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1769 - acc: 0.9236 - val_loss: 1.4962 - val_acc: 0.8448 Epoch 295/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1756 - acc: 0.9236 - val_loss: 1.4867 - val_acc: 0.8448 Epoch 296/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1769 - acc: 0.9170 - val_loss: 1.4942 - val_acc: 0.8448 Epoch 297/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1762 - acc: 0.9214 - val_loss: 1.5301 - val_acc: 0.8448 Epoch 298/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1765 - acc: 0.9214 - val_loss: 1.5150 - val_acc: 0.8473 Epoch 299/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1761 - acc: 0.9214 - val_loss: 1.5052 - val_acc: 0.8499 Epoch 300/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1754 - acc: 0.9236 - val_loss: 1.5169 - val_acc: 0.8473 Epoch 301/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1753 - acc: 0.9192 - val_loss: 1.5294 - val_acc: 0.8499 Epoch 302/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1756 - acc: 0.9236 - val_loss: 1.5208 - val_acc: 0.8499 Epoch 303/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1757 - acc: 0.9225 - val_loss: 1.5062 - val_acc: 0.8448 Epoch 304/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1756 - acc: 0.9236 - val_loss: 1.5205 - val_acc: 0.8524 Epoch 305/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1754 - acc: 0.9192 - val_loss: 1.5383 - val_acc: 0.8473 Epoch 306/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1756 - acc: 0.9203 - val_loss: 1.5172 - val_acc: 0.8473 Epoch 307/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1754 - acc: 0.9236 - val_loss: 1.5226 - val_acc: 0.8473 Epoch 308/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1764 - acc: 0.9214 - val_loss: 1.5300 - val_acc: 0.8499 Epoch 309/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1758 - acc: 0.9203 - val_loss: 1.5393 - val_acc: 0.8473 Epoch 310/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1771 - acc: 0.9214 - val_loss: 1.5173 - val_acc: 0.8448 Epoch 311/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1768 - acc: 0.9170 - val_loss: 1.5137 - val_acc: 0.8473 Epoch 312/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1770 - acc: 0.9203 - val_loss: 1.5349 - val_acc: 0.8499 Epoch 313/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1762 - acc: 0.9170 - val_loss: 1.5399 - val_acc: 0.8473 Epoch 314/500 916/916 [==============================] - 0s 78us/sample - loss: 0.1762 - acc: 0.9192 - val_loss: 1.5227 - val_acc: 0.8473 Epoch 315/500 916/916 [==============================] - 0s 80us/sample - loss: 0.1754 - acc: 0.9214 - val_loss: 1.5327 - val_acc: 0.8473 Epoch 316/500 916/916 [==============================] - 0s 80us/sample - loss: 0.1753 - acc: 0.9192 - val_loss: 1.5318 - val_acc: 0.8473 Epoch 317/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1773 - acc: 0.9225 - val_loss: 1.5377 - val_acc: 0.8473 Epoch 318/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1762 - acc: 0.9203 - val_loss: 1.5609 - val_acc: 0.8499 Epoch 319/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1764 - acc: 0.9170 - val_loss: 1.5582 - val_acc: 0.8499 Epoch 320/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1757 - acc: 0.9225 - val_loss: 1.5487 - val_acc: 0.8473 Epoch 321/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1774 - acc: 0.9192 - val_loss: 1.5438 - val_acc: 0.8473 Epoch 322/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1755 - acc: 0.9214 - val_loss: 1.5635 - val_acc: 0.8473 Epoch 323/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1752 - acc: 0.9203 - val_loss: 1.5677 - val_acc: 0.8499 Epoch 324/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1755 - acc: 0.9247 - val_loss: 1.5750 - val_acc: 0.8473 Epoch 325/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1758 - acc: 0.9203 - val_loss: 1.5338 - val_acc: 0.8499 Epoch 326/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1755 - acc: 0.9225 - val_loss: 1.5463 - val_acc: 0.8473 Epoch 327/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1755 - acc: 0.9225 - val_loss: 1.5657 - val_acc: 0.8473 Epoch 328/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1759 - acc: 0.9192 - val_loss: 1.5681 - val_acc: 0.8473 Epoch 329/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1760 - acc: 0.9181 - val_loss: 1.5357 - val_acc: 0.8473 Epoch 330/500 916/916 [==============================] - 0s 56us/sample - loss: 0.1759 - acc: 0.9170 - val_loss: 1.5573 - val_acc: 0.8448 Epoch 331/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1750 - acc: 0.9170 - val_loss: 1.5769 - val_acc: 0.8473 Epoch 332/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1762 - acc: 0.9170 - val_loss: 1.5825 - val_acc: 0.8499 Epoch 333/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1755 - acc: 0.9203 - val_loss: 1.5527 - val_acc: 0.8473 Epoch 334/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1757 - acc: 0.9203 - val_loss: 1.5513 - val_acc: 0.8499 Epoch 335/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1757 - acc: 0.9236 - val_loss: 1.5743 - val_acc: 0.8473 Epoch 336/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1754 - acc: 0.9225 - val_loss: 1.5822 - val_acc: 0.8499 Epoch 337/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1756 - acc: 0.9181 - val_loss: 1.5819 - val_acc: 0.8499 Epoch 338/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1755 - acc: 0.9214 - val_loss: 1.5691 - val_acc: 0.8448 Epoch 339/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1757 - acc: 0.9214 - val_loss: 1.5756 - val_acc: 0.8473 Epoch 340/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1757 - acc: 0.9214 - val_loss: 1.5837 - val_acc: 0.8499 Epoch 341/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1759 - acc: 0.9236 - val_loss: 1.5905 - val_acc: 0.8473 Epoch 342/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1767 - acc: 0.9214 - val_loss: 1.5831 - val_acc: 0.8473 Epoch 343/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1766 - acc: 0.9247 - val_loss: 1.6270 - val_acc: 0.8499 Epoch 344/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1764 - acc: 0.9170 - val_loss: 1.5843 - val_acc: 0.8473 Epoch 345/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1766 - acc: 0.9247 - val_loss: 1.5469 - val_acc: 0.8448 Epoch 346/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1758 - acc: 0.9192 - val_loss: 1.5584 - val_acc: 0.8473 Epoch 347/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1772 - acc: 0.9225 - val_loss: 1.5790 - val_acc: 0.8473 Epoch 348/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1758 - acc: 0.9214 - val_loss: 1.5540 - val_acc: 0.8473 Epoch 349/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1757 - acc: 0.9181 - val_loss: 1.5373 - val_acc: 0.8473 Epoch 350/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1751 - acc: 0.9236 - val_loss: 1.5599 - val_acc: 0.8473 Epoch 351/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1756 - acc: 0.9225 - val_loss: 1.5877 - val_acc: 0.8448 Epoch 352/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1764 - acc: 0.9203 - val_loss: 1.5739 - val_acc: 0.8422 Epoch 353/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1767 - acc: 0.9236 - val_loss: 1.5483 - val_acc: 0.8372 Epoch 354/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1752 - acc: 0.9214 - val_loss: 1.5750 - val_acc: 0.8397 Epoch 355/500
916/916 [==============================] - 0s 66us/sample - loss: 0.1757 - acc: 0.9214 - val_loss: 1.5983 - val_acc: 0.8422 Epoch 356/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1759 - acc: 0.9236 - val_loss: 1.5893 - val_acc: 0.8422 Epoch 357/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1756 - acc: 0.9170 - val_loss: 1.6191 - val_acc: 0.8422 Epoch 358/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1758 - acc: 0.9247 - val_loss: 1.5915 - val_acc: 0.8473 Epoch 359/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1756 - acc: 0.9181 - val_loss: 1.5914 - val_acc: 0.8473 Epoch 360/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1751 - acc: 0.9236 - val_loss: 1.6030 - val_acc: 0.8473 Epoch 361/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1757 - acc: 0.9181 - val_loss: 1.6202 - val_acc: 0.8422 Epoch 362/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1766 - acc: 0.9214 - val_loss: 1.5952 - val_acc: 0.8473 Epoch 363/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1753 - acc: 0.9214 - val_loss: 1.6140 - val_acc: 0.8499 Epoch 364/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1753 - acc: 0.9214 - val_loss: 1.6162 - val_acc: 0.8473 Epoch 365/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1757 - acc: 0.9203 - val_loss: 1.6176 - val_acc: 0.8422 Epoch 366/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1754 - acc: 0.9214 - val_loss: 1.6302 - val_acc: 0.8448 Epoch 367/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1754 - acc: 0.9225 - val_loss: 1.6396 - val_acc: 0.8422 Epoch 368/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1754 - acc: 0.9203 - val_loss: 1.6495 - val_acc: 0.8397 Epoch 369/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1754 - acc: 0.9203 - val_loss: 1.5839 - val_acc: 0.8422 Epoch 370/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1762 - acc: 0.9192 - val_loss: 1.5826 - val_acc: 0.8473 Epoch 371/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1752 - acc: 0.9214 - val_loss: 1.6003 - val_acc: 0.8448 Epoch 372/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1774 - acc: 0.9214 - val_loss: 1.6207 - val_acc: 0.8448 Epoch 373/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1752 - acc: 0.9214 - val_loss: 1.5903 - val_acc: 0.8473 Epoch 374/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1761 - acc: 0.9203 - val_loss: 1.5993 - val_acc: 0.8473 Epoch 375/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1768 - acc: 0.9192 - val_loss: 1.6700 - val_acc: 0.8397 Epoch 376/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1751 - acc: 0.9214 - val_loss: 1.6541 - val_acc: 0.8397 Epoch 377/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1750 - acc: 0.9192 - val_loss: 1.6022 - val_acc: 0.8397 Epoch 378/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1762 - acc: 0.9203 - val_loss: 1.6182 - val_acc: 0.8397 Epoch 379/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1767 - acc: 0.9192 - val_loss: 1.5386 - val_acc: 0.8422 Epoch 380/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1766 - acc: 0.9192 - val_loss: 1.5574 - val_acc: 0.8397 Epoch 381/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1759 - acc: 0.9214 - val_loss: 1.5500 - val_acc: 0.8422 Epoch 382/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1786 - acc: 0.9192 - val_loss: 1.6089 - val_acc: 0.8448 Epoch 383/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1825 - acc: 0.9214 - val_loss: 1.4379 - val_acc: 0.8397 Epoch 384/500 916/916 [==============================] - 0s 65us/sample - loss: 0.2642 - acc: 0.9017 - val_loss: 0.6020 - val_acc: 0.8372 Epoch 385/500 916/916 [==============================] - 0s 62us/sample - loss: 0.2421 - acc: 0.9028 - val_loss: 0.7005 - val_acc: 0.8473 Epoch 386/500 916/916 [==============================] - 0s 57us/sample - loss: 0.2090 - acc: 0.9170 - val_loss: 0.9440 - val_acc: 0.8550 Epoch 387/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1973 - acc: 0.9181 - val_loss: 1.2010 - val_acc: 0.8422 Epoch 388/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1893 - acc: 0.9225 - val_loss: 1.2154 - val_acc: 0.8422 Epoch 389/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1842 - acc: 0.9214 - val_loss: 1.1834 - val_acc: 0.8397 Epoch 390/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1799 - acc: 0.9225 - val_loss: 1.2968 - val_acc: 0.8422 Epoch 391/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1795 - acc: 0.9214 - val_loss: 1.3872 - val_acc: 0.8448 Epoch 392/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1784 - acc: 0.9214 - val_loss: 1.3459 - val_acc: 0.8422 Epoch 393/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1775 - acc: 0.9203 - val_loss: 1.3914 - val_acc: 0.8448 Epoch 394/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1780 - acc: 0.9236 - val_loss: 1.4126 - val_acc: 0.8422 Epoch 395/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1769 - acc: 0.9225 - val_loss: 1.4539 - val_acc: 0.8397 Epoch 396/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1778 - acc: 0.9214 - val_loss: 1.4453 - val_acc: 0.8397 Epoch 397/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1768 - acc: 0.9214 - val_loss: 1.4289 - val_acc: 0.8397 Epoch 398/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1765 - acc: 0.9203 - val_loss: 1.4386 - val_acc: 0.8397 Epoch 399/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1768 - acc: 0.9214 - val_loss: 1.4556 - val_acc: 0.8397 Epoch 400/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1761 - acc: 0.9214 - val_loss: 1.4582 - val_acc: 0.8397 Epoch 401/500 916/916 [==============================] - 0s 81us/sample - loss: 0.1770 - acc: 0.9203 - val_loss: 1.4882 - val_acc: 0.8397 Epoch 402/500 916/916 [==============================] - 0s 80us/sample - loss: 0.1769 - acc: 0.9225 - val_loss: 1.4969 - val_acc: 0.8397 Epoch 403/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1755 - acc: 0.9225 - val_loss: 1.5213 - val_acc: 0.8372 Epoch 404/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1757 - acc: 0.9225 - val_loss: 1.5230 - val_acc: 0.8397 Epoch 405/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1772 - acc: 0.9225 - val_loss: 1.5312 - val_acc: 0.8397 Epoch 406/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1754 - acc: 0.9203 - val_loss: 1.5458 - val_acc: 0.8397 Epoch 407/500 916/916 [==============================] - 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0s 65us/sample - loss: 0.1749 - acc: 0.9192 - val_loss: 1.6154 - val_acc: 0.8372 Epoch 438/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1761 - acc: 0.9192 - val_loss: 1.6359 - val_acc: 0.8397 Epoch 439/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1755 - acc: 0.9225 - val_loss: 1.6353 - val_acc: 0.8372 Epoch 440/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1768 - acc: 0.9236 - val_loss: 1.5965 - val_acc: 0.8372 Epoch 441/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1765 - acc: 0.9192 - val_loss: 1.6142 - val_acc: 0.8397 Epoch 442/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1753 - acc: 0.9203 - val_loss: 1.6112 - val_acc: 0.8397 Epoch 443/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1748 - acc: 0.9214 - val_loss: 1.6233 - val_acc: 0.8397 Epoch 444/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1749 - acc: 0.9203 - val_loss: 1.6256 - val_acc: 0.8397 Epoch 445/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1749 - acc: 0.9225 - val_loss: 1.6069 - val_acc: 0.8397 Epoch 446/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1751 - acc: 0.9225 - val_loss: 1.6302 - val_acc: 0.8397 Epoch 447/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1754 - acc: 0.9214 - val_loss: 1.6307 - val_acc: 0.8397 Epoch 448/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1758 - acc: 0.9214 - val_loss: 1.6535 - val_acc: 0.8422 Epoch 449/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1752 - acc: 0.9181 - val_loss: 1.6422 - val_acc: 0.8397 Epoch 450/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1751 - acc: 0.9170 - val_loss: 1.6619 - val_acc: 0.8397 Epoch 451/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1757 - acc: 0.9192 - val_loss: 1.6753 - val_acc: 0.8397 Epoch 452/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1758 - acc: 0.9203 - val_loss: 1.6790 - val_acc: 0.8422 Epoch 453/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1758 - acc: 0.9214 - val_loss: 1.6150 - val_acc: 0.8372 Epoch 454/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1747 - acc: 0.9192 - val_loss: 1.6245 - val_acc: 0.8397 Epoch 455/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1754 - acc: 0.9203 - val_loss: 1.6180 - val_acc: 0.8422 Epoch 456/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1764 - acc: 0.9225 - val_loss: 1.6302 - val_acc: 0.8397 Epoch 457/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1751 - acc: 0.9192 - val_loss: 1.6375 - val_acc: 0.8397 Epoch 458/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1754 - acc: 0.9192 - val_loss: 1.6502 - val_acc: 0.8397 Epoch 459/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1754 - acc: 0.9225 - val_loss: 1.6521 - val_acc: 0.8422 Epoch 460/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1761 - acc: 0.9214 - val_loss: 1.6323 - val_acc: 0.8422 Epoch 461/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1768 - acc: 0.9236 - val_loss: 1.6748 - val_acc: 0.8422 Epoch 462/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1755 - acc: 0.9192 - val_loss: 1.6472 - val_acc: 0.8397 Epoch 463/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1775 - acc: 0.9225 - val_loss: 1.6279 - val_acc: 0.8422 Epoch 464/500 916/916 [==============================] - 0s 82us/sample - loss: 0.1749 - acc: 0.9181 - val_loss: 1.6140 - val_acc: 0.8422 Epoch 465/500 916/916 [==============================] - 0s 101us/sample - loss: 0.1756 - acc: 0.9192 - val_loss: 1.6351 - val_acc: 0.8422 Epoch 466/500 916/916 [==============================] - 0s 78us/sample - loss: 0.1749 - acc: 0.9203 - val_loss: 1.6255 - val_acc: 0.8397 Epoch 467/500 916/916 [==============================] - 0s 70us/sample - loss: 0.1753 - acc: 0.9203 - val_loss: 1.6268 - val_acc: 0.8397 Epoch 468/500 916/916 [==============================] - 0s 71us/sample - loss: 0.1745 - acc: 0.9192 - val_loss: 1.6256 - val_acc: 0.8397 Epoch 469/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1749 - acc: 0.9214 - val_loss: 1.6280 - val_acc: 0.8372 Epoch 470/500 916/916 [==============================] - 0s 63us/sample - loss: 0.1750 - acc: 0.9203 - val_loss: 1.6328 - val_acc: 0.8397 Epoch 471/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1757 - acc: 0.9247 - val_loss: 1.6679 - val_acc: 0.8372 Epoch 472/500 916/916 [==============================] - 0s 58us/sample - loss: 0.1746 - acc: 0.9192 - val_loss: 1.6575 - val_acc: 0.8448 Epoch 473/500
916/916 [==============================] - 0s 64us/sample - loss: 0.1753 - acc: 0.9214 - val_loss: 1.6411 - val_acc: 0.8372 Epoch 474/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1756 - acc: 0.9192 - val_loss: 1.6347 - val_acc: 0.8372 Epoch 475/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1755 - acc: 0.9214 - val_loss: 1.6147 - val_acc: 0.8422 Epoch 476/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1767 - acc: 0.9192 - val_loss: 1.6462 - val_acc: 0.8372 Epoch 477/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1774 - acc: 0.9203 - val_loss: 1.5282 - val_acc: 0.8397 Epoch 478/500 916/916 [==============================] - 0s 67us/sample - loss: 0.1779 - acc: 0.9236 - val_loss: 1.5785 - val_acc: 0.8422 Epoch 479/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1765 - acc: 0.9214 - val_loss: 1.4288 - val_acc: 0.8372 Epoch 480/500 916/916 [==============================] - 0s 68us/sample - loss: 0.1753 - acc: 0.9181 - val_loss: 1.4766 - val_acc: 0.8422 Epoch 481/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1763 - acc: 0.9214 - val_loss: 1.5676 - val_acc: 0.8372 Epoch 482/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1763 - acc: 0.9192 - val_loss: 1.5566 - val_acc: 0.8397 Epoch 483/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1756 - acc: 0.9170 - val_loss: 1.5574 - val_acc: 0.8372 Epoch 484/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1748 - acc: 0.9214 - val_loss: 1.5717 - val_acc: 0.8397 Epoch 485/500 916/916 [==============================] - 0s 70us/sample - loss: 0.1751 - acc: 0.9225 - val_loss: 1.5697 - val_acc: 0.8372 Epoch 486/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1748 - acc: 0.9159 - val_loss: 1.5780 - val_acc: 0.8448 Epoch 487/500 916/916 [==============================] - 0s 77us/sample - loss: 0.1753 - acc: 0.9236 - val_loss: 1.5707 - val_acc: 0.8372 Epoch 488/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1760 - acc: 0.9225 - val_loss: 1.5876 - val_acc: 0.8397 Epoch 489/500 916/916 [==============================] - 0s 66us/sample - loss: 0.1743 - acc: 0.9203 - val_loss: 1.6118 - val_acc: 0.8397 Epoch 490/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1752 - acc: 0.9236 - val_loss: 1.5973 - val_acc: 0.8372 Epoch 491/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1753 - acc: 0.9247 - val_loss: 1.5828 - val_acc: 0.8372 Epoch 492/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1751 - acc: 0.9225 - val_loss: 1.5698 - val_acc: 0.8397 Epoch 493/500 916/916 [==============================] - 0s 59us/sample - loss: 0.1753 - acc: 0.9203 - val_loss: 1.5669 - val_acc: 0.8397 Epoch 494/500 916/916 [==============================] - 0s 62us/sample - loss: 0.1761 - acc: 0.9225 - val_loss: 1.5817 - val_acc: 0.8397 Epoch 495/500 916/916 [==============================] - 0s 61us/sample - loss: 0.1757 - acc: 0.9214 - val_loss: 1.6273 - val_acc: 0.8397 Epoch 496/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1756 - acc: 0.9203 - val_loss: 1.6319 - val_acc: 0.8372 Epoch 497/500 916/916 [==============================] - 0s 57us/sample - loss: 0.1754 - acc: 0.9236 - val_loss: 1.6400 - val_acc: 0.8346 Epoch 498/500 916/916 [==============================] - 0s 64us/sample - loss: 0.1749 - acc: 0.9214 - val_loss: 1.6391 - val_acc: 0.8372 Epoch 499/500 916/916 [==============================] - 0s 60us/sample - loss: 0.1748 - acc: 0.9214 - val_loss: 1.6791 - val_acc: 0.8346 Epoch 500/500 916/916 [==============================] - 0s 65us/sample - loss: 0.1753 - acc: 0.9225 - val_loss: 1.6735 - val_acc: 0.8372
pred=model_nn.predict_classes(X).astype(float)print(pred)[[4.87956345e-01 5.12043655e-01] [1.00000000e+00 0.00000000e+00] [9.99990702e-01 9.29832458e-06] ... [2.51247525e-01 7.48752475e-01] [3.47714871e-02 9.65228498e-01] [5.00362515e-01 4.99637485e-01]]
Pred=[]for j in pred: if (j[0]/j[1])>1: Pred.append(1) else: Pred.append(0) /home/abhijit/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in double_scalars This is separate from the ipykernel package so we can avoid doing imports until
print(Pred)[0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1]
from sklearn.tree import DecisionTreeClassifier clf = DecisionTreeClassifier(criterion = "gini", random_state = 100,max_depth=6, min_samples_leaf=8) clf.fit(X,Pred)DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=6,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=8, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False,
random_state=100, splitter='best')y_pred_tree = clf.predict(X_test) accuracy_score(Y_test,y_pred_tree)0.8651399491094147
from sklearn import treeimport graphviz tree_data = tree.export_graphviz(clf, out_file=None, feature_names=df_k.columns, class_names=["Survived","Not_Survived"], filled=True, rounded=True, special_characters=True) graph = graphviz.Source(tree_data) #this will create an iris.pdf file with the rule pathgraph.render("titanic")'titanic.pdf'
feat_importance = clf.tree_.compute_feature_importances(normalize=False)print("feat importance = " + str(feat_importance))feat importance = [0.00000000e+00 3.61136190e-03 1.39727482e-02 2.77128356e-01 0.00000000e+00 1.01837874e-02 3.36657108e-03 0.00000000e+00 3.66875884e-03 0.00000000e+00 4.96683522e-03 3.87192368e-03 0.00000000e+00 4.20667376e-04 4.60591655e-03 8.34158579e-03 0.00000000e+00 4.42596721e-03 6.48457847e-03 0.00000000e+00 8.54240790e-04 2.40717013e-04 0.00000000e+00 6.19429971e-04 6.36079009e-03 8.23858955e-05 0.00000000e+00 7.82349745e-03 3.09307607e-03 6.90860184e-03 0.00000000e+00]
print(len(feat_importance))31
print(df1.columns)Index(['Survived', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female',
'Sex_male', 'Embarked_C', 'Embarked_Q', 'Embarked_S', 'name_1',
'name_2', 'name_3', 'name_4', 'name_5', 'Siblings_0', 'Siblings_1',
'Parent_0', 'Parent_1', 'age_1', 'age_2', 'age_3', 'age_4', 'age_5',
'fare_1', 'fare_2', 'fare_3', 'fare_4', 'fare_5', 'ticket_0',
'ticket_1', 'cabin_0', 'cabin_1'],
dtype='object')
import matplotlib.pyplot as plt; plt.rcdefaults()import numpy as npimport matplotlib.pyplot as pltobjects = df_k.columnsy_pos = np.arange(len(objects))performance = feat_importancefig, ax = plt.subplots(figsize=(20, 20))plt.barh(y_pos, performance, align='center', alpha=0.5)fontsize=14,plt.yticks(y_pos, objects,fontsize=20)plt.xticks(fontsize=20)plt.xlabel('Contributions')plt.title('Feature Contributions',fontsize=20)plt.show()from sklearn.linear_model import LogisticRegressionmodel = LogisticRegression(solver='liblinear', random_state=0)model.fit(X, Pred)LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='warn', n_jobs=None, penalty='l2',
random_state=0, solver='liblinear', tol=0.0001, verbose=0,
warm_start=False)y_pred_tree = model.predict(X_test) accuracy_score(Y_test,y_pred_tree)0.8651399491094147
Weights=np.hstack((model.intercept_[:,None], model.coef_))print(Weights)[[ 0.12274084 0.5433339 0.21922863 -0.63982169 1.45315131 -1.33041046 0.0827389 0.22923408 -0.22390968 0.56095332 -1.14391015 -0.04797768 1.28082004 -0.52714469 0.30183458 -0.17909374 0.37075839 -0.24801754 0.37928751 0.22380996 0.12957386 -0.51560858 -0.09432191 -0.18315589 -0.00641827 0.49388734 -0.09299957 -0.08857277 0.10037632 0.02236453 -0.34099133 0.46373217]]
print(len(Weights[0]))32
print(df1.columns)Index(['Survived', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female',
'Sex_male', 'Embarked_C', 'Embarked_Q', 'Embarked_S', 'name_1',
'name_2', 'name_3', 'name_4', 'name_5', 'Siblings_0', 'Siblings_1',
'Parent_0', 'Parent_1', 'age_1', 'age_2', 'age_3', 'age_4', 'age_5',
'fare_1', 'fare_2', 'fare_3', 'fare_4', 'fare_5', 'ticket_0',
'ticket_1', 'cabin_0', 'cabin_1'],
dtype='object')
s=np.sum(Weights)import matplotlib.pyplot as plt; plt.rcdefaults()import numpy as npimport matplotlib.pyplot as pltfeat=df_k.columnsprint(feat)objects=feat.insert(0,'Constant')print(objects)y_pos = np.arange(len(objects))performance = Weights[0]fig, ax = plt.subplots(figsize=(20, 20))plt.barh(y_pos, performance, align='center', alpha=0.5)fontsize=14,plt.yticks(y_pos, objects,fontsize=20)plt.xticks(fontsize=20)plt.xlabel('Contributions')plt.title('Feature Contributions',fontsize=20)plt.show()Index(['Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female', 'Sex_male',
'Embarked_C', 'Embarked_Q', 'Embarked_S', 'name_1', 'name_2', 'name_3',
'name_4', 'name_5', 'Siblings_0', 'Siblings_1', 'Parent_0', 'Parent_1',
'age_1', 'age_2', 'age_3', 'age_4', 'age_5', 'fare_1', 'fare_2',
'fare_3', 'fare_4', 'fare_5', 'ticket_0', 'ticket_1', 'cabin_0',
'cabin_1'],
dtype='object')
Index(['Constant', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female',
'Sex_male', 'Embarked_C', 'Embarked_Q', 'Embarked_S', 'name_1',
'name_2', 'name_3', 'name_4', 'name_5', 'Siblings_0', 'Siblings_1',
'Parent_0', 'Parent_1', 'age_1', 'age_2', 'age_3', 'age_4', 'age_5',
'fare_1', 'fare_2', 'fare_3', 'fare_4', 'fare_5', 'ticket_0',
'ticket_1', 'cabin_0', 'cabin_1'],
dtype='object')